E-commerce monolith migration

[SYSTEM]
You are an expert assistant in complex project planning.
Your task is to generate a detailed and structured action plan to reach the main objective in the most professional and most detailed way, no matter how much work or steps will be needed to perform.
Use your internal reasoning processes to think deeply about the problem and create the most comprehensive plan possible.
Take as much time and space as you need to think through all aspects of the problem.
After your thorough analysis, answer with the plan in the requested structure.

Every text field you write will be read by a busy person, so write for them: short sentences, one idea each, in short paragraphs. Text fields accept Markdown: separate paragraphs with a blank line, use a bullet list when you enumerate things and plain paragraphs when you explain or argue, and bold at most one key phrase per paragraph. A text of more than three sentences must be split into paragraphs; never deliver a single unbroken block of text.

[HUMAN]
Main Objective: "A mid-size European fashion retailer runs its e-commerce on a 10-year-old monolith: Java 8 / Spring, about 2 million lines, one PostgreSQL database of 1.2 TB with 350 tables and heavy use of stored procedures and cross-module joins. It serves 8 countries, 3 currencies and 4 languages, with about 40,000 orders a day and peaks of 12x during sales. The monolith contains: the storefront (server-rendered, plus a separate mobile app hitting the same endpoints), catalogue and search (a Lucene index rebuilt nightly), pricing and promotions (the most complex module, 200,000 lines, with country-specific rules nobody fully understands), cart and checkout with three payment providers, order management, inventory synchronised every 15 minutes with the warehouse system via file exchange, customer accounts and loyalty, returns, and a back-office used by 300 staff. Deployments happen every two weeks as a single artefact with a 30-minute maintenance window; test coverage is 25% and mostly unit tests. Five teams of 8 developers, each organised around a business area but all committing to the same repository.

Plan the migration to independently deployable services over 12 months with no feature freeze, no unplanned downtime and the ability to roll back every step, preserving the peak-season capacity (sales in January and July must not be put at risk)."

Generate an initial, detailed, and well-structured plan proposal.

The plan has these parts:
- "steps": the list of steps, each with "step_id" (a short label unique within this proposal, such as S1, S2, S3), "title" (a short, descriptive title), "description" (what the step does and how, in Markdown: a short opening paragraph and then, if there are several concrete things to say, a bullet list) and "dependencies" (the step_ids of the other steps of THIS proposal that must be completed first; empty if none).
- "estimated_complexity": "low", "medium" or "high".
- "success_metrics": clear and measurable success metrics, one per line as a bullet list.

Think deeply about the problem, considering all aspects and implications. Develop a comprehensive understanding of what needs to be done to achieve the objective, then produce the plan.

{"steps": [{"step_id": "S1", "title": "Establish governance and migration steering committee", "description": "Create a governance structure to guide the 12-month migration and ensure alignment across teams.\n\n- Define clear decision-making authority and escalation paths.\n- Establish weekly steering meetings with representatives from each of the five teams plus leadership.\n- Create a shared vision for service boundaries and prioritize which modules to extract first.\n- Set up RACI matrix (responsible, accountable, consulted, informed) for each major service extraction.", "dependencies": []}, {"step_id": "S2", "title": "Design service architecture and system boundaries", "description": "Map the monolith into independently deployable services with clear boundaries and synchronization points.\n\n- Analyze the 350 tables and identify which tables belong to each business domain (catalog, pricing, orders, inventory, etc.).\n- Design the data synchronization strategy for the 1.2 TB database, including which data moves to which service.\n- Plan the strangler approach for each module: what gets extracted first, what depends on what.\n- Define API contracts and asynchronous messaging patterns (events vs. direct calls) between services.", "dependencies": ["S1"]}, {"step_id": "S3", "title": "Deploy Kubernetes infrastructure and container registry", "description": "Build the cloud infrastructure to run containerized services at scale with redundancy and monitoring.\n\n- Provision a production-grade Kubernetes cluster (managed service like EKS, AKS, or on-premises).\n- Set up container image registry with retention policies and security scanning.\n- Configure persistent storage volumes for databases and caches.\n- Implement cluster networking, RBAC, and network policies for security.", "dependencies": ["S2"]}, {"step_id": "S4", "title": "Implement strangler proxy and API gateway", "description": "Deploy a reverse proxy that routes requests between the monolith and the new services, enabling gradual traffic migration.\n\n- Deploy API gateway (e.g., Kong, Ambassador, or cloud-native option) in front of the monolith.\n- Implement request routing logic to direct traffic to services or monolith based on rules.\n- Add request/response logging and transformation layer for API versioning.\n- Enable instant traffic rerouting and rollback if a service fails.", "dependencies": ["S3"]}, {"step_id": "S5", "title": "Build feature flagging and traffic routing system", "description": "Implement a system to control which users hit which service, enabling safe canary deployments and A/B testing.\n\n- Choose or build a feature flag platform (LaunchDarkly, Unleash, or open source).\n- Integrate with API gateway and service code to support percentage-based traffic splitting.\n- Enable per-user, per-region, and per-browser routing for testing (e.g., mobile app to new service before web).\n- Create dashboards for ops teams to adjust routing in real time without redeploying.", "dependencies": ["S3"]}, {"step_id": "S6", "title": "Establish comprehensive observability (logs, metrics, tracing)", "description": "Deploy centralized logging, metrics, and tracing to track behavior across services and detect issues early.\n\n- Set up centralized log aggregation (e.g., ELK, Splunk, or cloud-native solution).\n- Deploy metrics collection (Prometheus, Datadog, or equivalent) with dashboards for each service.\n- Implement distributed tracing (Jaeger, Zipkin) to track requests across service boundaries.\n- Define critical alerts: error rates, latency spikes, database query performance, payment transaction failures.", "dependencies": ["S3"]}, {"step_id": "S7", "title": "Design event-driven data consistency architecture", "description": "Plan how services will stay in sync when sharing data extracted from the monolith's single database.\n\n- Design an event bus or message queue topology (Kafka, RabbitMQ, or cloud equivalent).\n- Plan Change Data Capture (CDC) from the monolith to notify services when data changes.\n- Define saga patterns for multi-step distributed transactions (e.g., order creation spanning multiple services).\n- Document how to handle eventual consistency, conflicts, and zombie data in each service.", "dependencies": ["S3"]}, {"step_id": "S8", "title": "Build inter-service communication framework (APIs and queues)", "description": "Establish libraries and standards for how services talk to each other synchronously and asynchronously.\n\n- Define REST or gRPC standards (authentication, versioning, error handling) for all service-to-service calls.\n- Create shared libraries for message publishing/consuming (idempotency, dead-letter handling).\n- Document timeout and retry policies to prevent cascading failures.\n- Provide templates and SDKs to development teams so they don't reimplement these patterns.", "dependencies": ["S3"]}, {"step_id": "S9", "title": "Extract catalog and search service", "description": "Extract the catalog and Lucene search index into its own service, starting with a low-risk module to validate the pattern.\n\n- Move catalog module code from monolith to a new service repository.\n- Containerize the service and deploy to Kubernetes.\n- Keep the existing Lucene index and nightly rebuild process initially.\n- Route catalog API requests through the gateway: send 10% of traffic to new service first, validate results, increase to 100%.", "dependencies": ["S4", "S5", "S6", "S8"]}, {"step_id": "S10", "title": "Create independent catalog data layer with synchronization", "description": "Extract catalog tables from the shared database and sync changes from the monolith to the new service.\n\n- Copy catalog tables to a new PostgreSQL database managed by the catalog service.\n- Implement CDC (Change Data Capture) to publish catalog changes as events when the monolith updates data.\n- Build catalog service to subscribe to these events and update its own tables.\n- Implement consistency checks: run hourly validation that catalog service data matches monolith source-of-truth, log discrepancies.", "dependencies": ["S9", "S7"]}, {"step_id": "S11", "title": "Extract customer accounts service", "description": "Move customer profile, login, and loyalty data into a dedicated service that other services query.\n\n- Extract customer and loyalty tables from monolith database.\n- Build service to manage customer profile, authentication, and loyalty points.\n- Implement event stream for customer changes (profile updates, loyalty point transactions).\n- Route customer API calls through gateway; monolith and new service share database briefly, then switch to CDC sync.", "dependencies": ["S4", "S5", "S6", "S8"]}, {"step_id": "S12", "title": "Extract returns management service", "description": "Create a focused returns processing service to further validate the extraction pattern and learn before tackling complex modules.\n\n- Move returns processing logic and tables from monolith.\n- Build simple service with clear inputs (return requests) and outputs (refund events).\n- Connect to order data via API calls (will be extracted separately) and inventory service.\n- Canary traffic, monitor error rates and latency; this is the lowest-risk extraction.", "dependencies": ["S4", "S5", "S6", "S8"]}, {"step_id": "S13", "title": "Audit, document, and decompose pricing/promotions business rules", "description": "Reverse-engineer and document the complex pricing logic to enable rebuilding it as a new service. Start early in parallel with infrastructure work.\n\n- Form a task force: architects, the original pricing team, and business analysts.\n- Read through the 200k lines of pricing code; document country-specific rules, exceptions, and dependencies (which rules call which).\n- Build a comprehensive spreadsheet of pricing scenarios: free shipping rules, discount types, country-specific taxes, dynamic pricing, etc.\n- Extract test cases from production data: get 1,000 real orders from each country and document how pricing rules applied.\n- Identify which pricing decisions depend on cart, inventory, or customer account data.", "dependencies": ["S1"]}, {"step_id": "S14", "title": "Design and implement pricing/promotions service with enhanced testing", "description": "Rebuild the pricing logic as a new microservice with a cleaner architecture and comprehensive test coverage.\n\n- Architect the new service with clear separation: promotion evaluation, tax calculation, discount application, price transformation per country.\n- Implement each country's rules as either code or a rules engine (not hardcoded strings).\n- Build unit tests for 100+ pricing scenarios (cross-reference with S13 test cases).\n- Implement shadow traffic testing: send real production requests to both monolith and new service, log differences, investigate discrepancies before switching traffic.", "dependencies": ["S4", "S5", "S6", "S8", "S13"]}, {"step_id": "S15", "title": "Implement event-driven pricing and cart synchronization", "description": "Sync pricing changes and promotions between the pricing service and cart/checkout to keep pricing consistent in real time.\n\n- Publish events when promotions are created/updated: promotion_created, promotion_updated, promotion_ended.\n- Implement cart service subscription: when a cart is modified or promotion changes, recalculate cart total.\n- Handle time-based promotions: if a promotion starts/ends during a customer's shopping, reflect immediately.\n- Validate consistency: sample 1% of checkouts, compare price calculated by pricing service vs. what customer paid; alert if mismatch.", "dependencies": ["S14", "S7", "S9"]}, {"step_id": "S16", "title": "Extract inventory management service", "description": "Create a service that manages stock levels and warehouse synchronization, replacing the 15-minute batch sync with event-driven updates.\n\n- Extract inventory tables and warehouse sync logic from monolith.\n- Build inventory service that subscribes to warehouse file drops (replace file exchange with event publishing or direct API).\n- Implement real-time inventory updates: when an order is placed, reserve stock immediately; when warehouse sends stock count, update available qty.\n- Canary deploy and validate: monitor for stock mismatch errors (overselling); maintain monolith as source-of-truth with service as secondary initially.", "dependencies": ["S4", "S5", "S6", "S8", "S10"]}, {"step_id": "S17", "title": "Extract payment gateway coordination service", "description": "Abstract the three payment providers into a dedicated service so checkout doesn't depend on external API details.\n\n- Move payment provider logic (Stripe, PayPal, local provider) from monolith checkout to new service.\n- Implement payment orchestration: route to correct provider based on country/currency, handle failures, retry logic.\n- Build payment event stream: payment_initiated, payment_authorized, payment_captured, payment_failed, payment_refunded.\n- Test thoroughly: use sandbox accounts, simulate failure scenarios (provider timeout, decline, network error); ensure consistent error messages to checkout.\n- Use gateway to route: send payments for test users/regions to new service first.", "dependencies": ["S4", "S5", "S6", "S8"]}, {"step_id": "S18", "title": "Implement resilience patterns across services (circuit breakers, fallbacks, retries)", "description": "Make services robust to failures of dependent services; services should handle failures gracefully, not crash the whole system.\n\n- Install circuit breaker library (Resilience4j, Hystrix equivalent) in each service.\n- Define circuit breaker policies per dependency: if catalog service is slow, circuit opens after 50 failures or 5 seconds slow response, fails fast.\n- Implement fallback strategies: if pricing service is down, use cached pricing; if inventory is down, temporarily increase order-to-fulfillment delay.\n- Set timeouts on all cross-service calls (e.g., cart→pricing must return in 500ms) with bulkhead pattern to prevent resource exhaustion.\n- Test: use chaos monkey or chaos toolkit to inject failures (kill pods, add latency) and verify fallbacks work.", "dependencies": ["S9", "S10", "S11", "S12"]}, {"step_id": "S19", "title": "Build comprehensive integration test suite", "description": "Create automated tests that exercise real customer journeys across multiple services to catch bugs before production.\n\n- Build test data setup: create products, customers, promos, inventory in test environment.\n- Write end-to-end test scenarios: browse catalog → add to cart → apply promo → checkout with payment → order created → inventory updated → returns processing.\n- Implement performance tests: simulate 40,000 orders/day baseline load, 480,000 orders (12x peak) burst load; validate response times and error rates.\n- Add chaos tests: run scenarios while services fail (pod restart, network partition, database slow) to validate resilience.\n- Run tests on every service commit and nightly against staging environment; alert on test failure.", "dependencies": ["S14", "S16", "S17"]}, {"step_id": "S20", "title": "Create independent service deployment pipelines", "description": "Set up automated deployment so each service can be released independently without coordinating with other teams every two weeks.\n\n- For each service: build → run tests → build container image → push to registry → deploy to staging with canary (5% traffic initially).\n- Implement automated rollback: if error rate on new service exceeds threshold for 5 minutes, automatically route traffic back to old version and alert.\n- Add manual approval gates for production: team lead reviews test results, approves, release happens with 0 downtime (health checks, graceful shutdown).\n- Documentation: each team has runbook for deploying their service, rolling back, handling incidents.\n- Target: enable each team to deploy 1-2 times per day if needed.", "dependencies": ["S4", "S18"]}, {"step_id": "S21", "title": "Conduct load testing and peak-season capacity planning", "description": "Validate that the new service architecture can handle peak loads (40k baseline, 480k at 12x peak) without degradation.\n\n- Load test in staging: ramp up traffic gradually, measure latency, error rate, and resource usage (CPU, memory, database connections).\n- Identify bottlenecks: where does latency spike first? Is it database queries, service CPU, or network?\n- For each service and the database: determine max capacity and burst capacity (e.g., catalog service handles 500 QPS sustained, 1000 QPS for 30s burst).\n- Plan auto-scaling: set Kubernetes horizontal pod autoscaler min/max replicas, database read replicas, and caching layers based on results.\n- Validate payment processing: simulate peak payment volume with all three providers; confirm no payments are lost or duplicated.", "dependencies": ["S19", "S20"]}, {"step_id": "S22", "title": "Execute comprehensive pre-peak-season validation and simulation", "description": "Run a full-dress rehearsal before January/July peak season to ensure the system is ready; critical gate before any further changes.\n\n- Schedule 48-hour end-to-end test: run production-like load against all services with canary deployments to catch integration issues.\n- Run disaster recovery drill: if one service is down, can customers still browse and checkout? If payment provider fails, can we use fallback?\n- Customer journey validation: have real team members and friendly customers test: browse → add items → apply promo code → checkout on web and mobile apps.\n- Team readiness: hold incident response drill, confirm runbooks are accurate, escalation paths clear, and alert thresholds are tuned.\n- Performance sign-off: confirm P95 page load times, checkout flow time, order processing latency all meet SLOs for peak traffic.\n- Go/no-go decision: leadership reviews results; if any critical issue, fix and re-test before peak season starts.", "dependencies": ["S21"]}, {"step_id": "S23", "title": "Monitor, optimize, and prepare for ongoing evolution", "description": "After validation, monitor the production system closely during peak season; optimize based on real behavior and plan next improvements.\n\n- Daily monitoring during peak season: dashboard watching error rates, latency, payment success rate, customer support tickets.\n- Real-time tuning: if one service is bottleneck, increase replicas or add caching; if database query is slow, add index (non-blocking).\n- Post-peak analysis: compare actual peak performance to projections; document what assumptions were wrong, what worked well.\n- Identify quick wins: which cross-service calls can be cached, which synchronous calls could be async, which services could be split further?\n- Plan the next 6 months: which remaining monolith modules can be extracted, which services need optimization, should we change tech stack for any?", "dependencies": ["S22"]}], "estimated_complexity": "high", "success_metrics": "- All major business functions (catalog, pricing, orders, payments, inventory) deployed as independently releasable services by end of month 12.\n- Deployment frequency increased from bi-weekly to daily or on-demand per team, with zero coordination required between teams.\n- Peak-season capacity maintained: handle 40,000 orders/day baseline and 480,000 orders/day (12x peak) with P95 page load time ≤2 seconds and payment success rate >99.9%.\n- Zero unplanned downtime caused by the migration during the 12-month period; any incidents are handled with automated rollback within 5 minutes.\n- Test coverage increased from 25% to ≥70% across all services, with comprehensive integration test suite running on every commit.\n- Mean time to recovery (MTTR) for production incidents reduced from hours to <15 minutes due to circuit breakers and fallbacks.\n- Data consistency validated: automatic nightly checks confirm service data matches source-of-truth, with any discrepancies logged and investigated within 24 hours.\n- Service API latency (p95) meets SLOs: catalog ≤200ms, pricing ≤300ms, inventory ≤200ms, payment ≤1000ms, with circuit breakers preventing cascading failures.\n- Each service has documented runbooks, incident response procedures, and scaling playbooks; all ops and dev teams trained and confident.\n- Feature delivery velocity maintained at pre-migration levels: new feature deployment time remains unchanged despite architectural changes."}

[SYSTEM]
You are an expert assistant in complex project planning.
Your task is to generate a detailed and structured action plan to reach the main objective in the most professional and most detailed way, no matter how much work or steps will be needed to perform.
Use your internal reasoning processes to think deeply about the problem and create the most comprehensive plan possible.
Take as much time and space as you need to think through all aspects of the problem.
After your thorough analysis, answer with the plan in the requested structure.

Every text field you write will be read by a busy person, so write for them: short sentences, one idea each, in short paragraphs. Text fields accept Markdown: separate paragraphs with a blank line, use a bullet list when you enumerate things and plain paragraphs when you explain or argue, and bold at most one key phrase per paragraph. A text of more than three sentences must be split into paragraphs; never deliver a single unbroken block of text.

[HUMAN]
Main Objective: "A mid-size European fashion retailer runs its e-commerce on a 10-year-old monolith: Java 8 / Spring, about 2 million lines, one PostgreSQL database of 1.2 TB with 350 tables and heavy use of stored procedures and cross-module joins. It serves 8 countries, 3 currencies and 4 languages, with about 40,000 orders a day and peaks of 12x during sales. The monolith contains: the storefront (server-rendered, plus a separate mobile app hitting the same endpoints), catalogue and search (a Lucene index rebuilt nightly), pricing and promotions (the most complex module, 200,000 lines, with country-specific rules nobody fully understands), cart and checkout with three payment providers, order management, inventory synchronised every 15 minutes with the warehouse system via file exchange, customer accounts and loyalty, returns, and a back-office used by 300 staff. Deployments happen every two weeks as a single artefact with a 30-minute maintenance window; test coverage is 25% and mostly unit tests. Five teams of 8 developers, each organised around a business area but all committing to the same repository.

Plan the migration to independently deployable services over 12 months with no feature freeze, no unplanned downtime and the ability to roll back every step, preserving the peak-season capacity (sales in January and July must not be put at risk)."

Generate an initial, detailed, and well-structured plan proposal.

The plan has these parts:
- "steps": the list of steps, each with "step_id" (a short label unique within this proposal, such as S1, S2, S3), "title" (a short, descriptive title), "description" (what the step does and how, in Markdown: a short opening paragraph and then, if there are several concrete things to say, a bullet list) and "dependencies" (the step_ids of the other steps of THIS proposal that must be completed first; empty if none).
- "estimated_complexity": "low", "medium" or "high".
- "success_metrics": clear and measurable success metrics, one per line as a bullet list.

Think deeply about the problem, considering all aspects and implications. Develop a comprehensive understanding of what needs to be done to achieve the objective, then produce the plan.

{"steps": [{"step_id": "S1", "title": "Establish migration governance and delivery model", "description": "Create a migration programme that protects revenue, peak periods, and ongoing feature delivery. Assign one accountable programme lead, a chief architect, and named business and operational owners for every domain.\n\n- Create a steering group with engineering, product, operations, security, finance, warehouse, payments, and country representatives.\n- Reserve capacity per team: 50% business delivery, 30% migration work, and 20% quality, operational, and unplanned-work reduction. Rebalance only through the steering group.\n- Publish decision rights, architecture principles, risk register, dependency board, and weekly programme cadence.\n- Define explicit stop/go criteria for each production cutover and a formal rollback authority.\n- Plan sales protection windows: no first-time domain cutovers, database schema changes, payment changes, or major traffic experiments during the four weeks before and through January and July sales periods.\n- Keep feature work flowing through the same delivery pipeline, with feature flags used to decouple code deployment from customer release.", "dependencies": []}, {"step_id": "S2", "title": "Baseline the monolith, traffic, data, and operational risk", "description": "Build an evidence-based picture of the current system before selecting extraction order. The baseline becomes the capacity, correctness, and rollback reference for every migration wave.\n\n- Map request flows from web, mobile, back-office, warehouse files, payment providers, and scheduled jobs to modules, tables, stored procedures, queues, and external dependencies.\n- Measure normal and sale-peak throughput, latency, error rates, database load, index rebuild duration, batch duration, payment approval rates, and recovery times.\n- Classify all 350 tables and stored procedures by owning business concept, writers, readers, sensitivity, retention requirements, and cross-module coupling.\n- Identify critical business invariants, including stock reservation, price calculation, promotion eligibility, payment-to-order consistency, returns, loyalty accrual, and country tax requirements.\n- Produce dependency heat maps and a candidate extraction scorecard using coupling, business risk, change frequency, data ownership feasibility, and expected value.\n- Capture a production-like anonymised data set and documented peak-load profiles for repeatable testing.", "dependencies": ["S1"]}, {"step_id": "S3", "title": "Define target architecture and domain boundaries", "description": "Agree a pragmatic target architecture based on bounded contexts, clear data ownership, and incremental extraction. Do not start by redesigning every business process or splitting every table.\n\n- Define initial bounded contexts: edge/storefront experience, catalogue, search, pricing and promotions, cart, checkout, payments, orders, inventory, customers and loyalty, returns, and back-office workflow.\n- Assign a single system of record and an owning team for each business data entity. Services may consume replicated data but must not directly write another service's database.\n- Define synchronous API rules, asynchronous event rules, versioning rules, idempotency requirements, correlation identifiers, and error-handling conventions.\n- Establish a platform pattern: containerised services, managed or highly available PostgreSQL where appropriate, API gateway or edge routing, event transport, secrets management, central configuration, and infrastructure as code.\n- Select an incremental strangler pattern. New services are introduced behind stable interfaces while the monolith remains the source of truth until ownership is deliberately transferred.\n- Document explicitly that distributed transactions are prohibited. Use outbox, idempotent consumers, compensating actions, reconciliation, and business-visible exception queues instead.", "dependencies": ["S2"]}, {"step_id": "S4", "title": "Create production safety foundations", "description": "Make every current and future component observable, operable, and auditable before material traffic is moved. This work starts in the monolith as well as in new services.\n\n- Implement standard structured logs, metrics, distributed tracing, correlation IDs, service dashboards, synthetic customer journeys, and business KPIs.\n- Define service-level objectives for storefront availability, search, price response, cart operations, checkout, payment confirmation, order creation, and warehouse export.\n- Add alerting with severity, ownership, escalation paths, and tested runbooks. Alert on business failures as well as infrastructure failures.\n- Establish immutable audit events for pricing changes, promotion decisions, payments, order state changes, stock adjustments, customer-data access, and administrative actions.\n- Implement backup, restore, disaster recovery, and failover tests for the monolith database, new data stores, event platform, and search platform.\n- Create a shared operations readiness review required before any service receives production traffic.", "dependencies": ["S1", "S3"]}, {"step_id": "S5", "title": "Build secure delivery and runtime platform", "description": "Provide a paved road for independently deployable services. The platform must reduce deployment risk rather than create a second operational burden.\n\n- Build standard service templates for Java, including health checks, readiness checks, graceful shutdown, telemetry, API documentation, authentication, configuration, database migrations, and outbox publishing.\n- Implement CI/CD with build provenance, dependency and container scanning, automated unit, contract, integration, and smoke tests, environment promotion, and approval controls for high-risk releases.\n- Provision isolated development, integration, staging, performance, and production environments through infrastructure as code.\n- Introduce progressive delivery capabilities: feature flags, canary releases, blue/green deployment where justified, traffic splitting, automated rollback, and deployment freeze controls.\n- Centralise secrets, certificate rotation, least-privilege service identities, encryption, vulnerability management, and GDPR data-handling controls.\n- Ensure platform capacity is sized and load-tested for at least the documented 12x sales peak plus agreed headroom.", "dependencies": ["S3", "S4"]}, {"step_id": "S6", "title": "Improve monolith safety while it remains live", "description": "Stabilise the monolith so it can safely coexist with extracted services for most of the programme. The monolith remains a production dependency and needs the same operational discipline as new services.\n\n- Add a modularity boundary map and enforce it with architecture tests, package rules, code ownership, and mandatory reviews for cross-module changes.\n- Wrap high-risk database access behind repository or application interfaces, beginning with areas selected for extraction.\n- Introduce expand-contract database migration rules. Additive, backward-compatible changes deploy first; destructive changes require evidence that all readers have moved.\n- Raise automated regression coverage around critical journeys before touching them, using API, integration, and end-to-end tests rather than relying only on unit tests.\n- Add feature flags and kill switches around all new monolith-to-service integrations.\n- Reduce the 30-minute maintenance dependency by proving online deployment procedures, connection draining, backward-compatible schema releases, and zero-downtime smoke tests.", "dependencies": ["S2", "S4", "S5"]}, {"step_id": "S7", "title": "Implement integration, event, and data-transition patterns", "description": "Create reusable patterns for safe coexistence between the monolith and services. This is the core mechanism for reversible migration without dual-write corruption.\n\n- Introduce an event backbone and schema registry or equivalent governance, with versioned events, retention policies, dead-letter handling, replay procedures, and consumer ownership.\n- Implement transactional outbox publishing in the monolith and each service. Events are committed with source data and delivered asynchronously with deduplication.\n- Provide change-data-capture only where an outbox cannot initially be added, with monitoring and a time-bound plan to replace it.\n- Build a replication and reconciliation framework that compares source and target counts, hashes, business totals, lag, and exception records.\n- Standardise anti-corruption adapters so services do not inherit monolith-specific data shapes and semantics.\n- Define transition states for each entity: monolith-owned, replicated read model, dual-read validation, service-owned with monolith compatibility adapter, and legacy-retired.", "dependencies": ["S3", "S5", "S6"]}, {"step_id": "S8", "title": "Create quality, performance, and release assurance", "description": "Replace confidence based on a fortnightly monolith release with automated evidence for each independently deployed component. Focus first on revenue-critical and migration-affected flows.\n\n- Build a production-like test environment with anonymised data, external-provider simulators, warehouse-file simulators, and repeatable country, currency, language, tax, and promotion test fixtures.\n- Establish consumer-driven API and event contract tests. Producers may not release breaking changes until consumers have migrated or compatibility periods expire.\n- Create end-to-end tests for browse-to-order, guest and registered checkout, payment success and failure, cancellation, return, refund, stock changes, loyalty, and back-office operations.\n- Implement load, soak, spike, chaos, and failover tests using the observed 12x sale profile. Run them before every traffic expansion.\n- Use shadow execution for high-risk decisions. Compare service and monolith outputs without changing customer outcomes.\n- Set release gates for security, contracts, performance, observability, rollback rehearsal, and business reconciliation.", "dependencies": ["S2", "S4", "S5", "S7"]}, {"step_id": "S9", "title": "Select and sequence extraction waves", "description": "Prioritise small, low-coupling seams first, then use the resulting capabilities for harder domains. Pricing, promotions, checkout, and core order ownership are deliberately not first-wave candidates.\n\n- Wave 1: edge routing, read-only catalogue API, search, and selected back-office read/reporting capabilities.\n- Wave 2: inventory availability read model and warehouse integration adapter, while preserving the current order and stock authority initially.\n- Wave 3: customer profile and selected loyalty read/write capabilities, subject to GDPR and identity constraints.\n- Wave 4: order query model, notification or non-core order workflow, and returns workflow where process boundaries are confirmed.\n- Wave 5: cart and checkout façade components, followed by payment-provider adapters only after reliability evidence is sufficient.\n- Treat pricing and promotions as a dedicated discovery-and-modernisation stream. Extract only verified, bounded slices after exhaustive parity testing; retain the monolith engine behind an API if full extraction is not safe within 12 months.\n- Define per-wave entry criteria, exit criteria, capacity allocation, and a no-go rule for work that would cross a sales protection window.", "dependencies": ["S2", "S3", "S8"]}, {"step_id": "S10", "title": "Introduce edge routing and façade interfaces", "description": "Decouple channels from monolith internals before extracting business capabilities. Web, mobile, and back-office clients must use stable, versioned interfaces rather than service-specific implementation details.\n\n- Place an API gateway or backend-for-frontend layer in front of existing endpoints without changing functional behaviour.\n- Route by path, tenant/country, customer cohort, feature flag, and percentage of traffic. Default routing remains to the monolith.\n- Add authentication, authorisation, rate limiting, request validation, response compatibility, correlation IDs, and traffic dashboards at the edge.\n- Preserve mobile API compatibility through versioning and adapter endpoints. Do not force a mobile release as a prerequisite for backend extraction.\n- Implement instant route rollback to the monolith, including tested handling for sessions, carts, cached responses, and in-flight requests.\n- Measure baseline response equivalence and latency overhead before moving any business endpoint.", "dependencies": ["S4", "S5", "S6", "S8"]}, {"step_id": "S11", "title": "Extract catalogue read API and modern search", "description": "Deliver the first customer-facing extraction through read-heavy capabilities with clear fallback paths. This validates the platform, routing, replication, and operational model without changing transaction ownership.\n\n- Build a catalogue read service fed from monolith-owned catalogue data through outbox or controlled replication.\n- Replace nightly-only Lucene rebuilding with an independently operated search service that supports incremental index updates, aliases, blue/green indexes, and rapid rollback to the existing index.\n- Run catalogue and search in shadow mode. Compare product availability, locale content, ranking, facets, response time, and zero-result rates against current behaviour.\n- Shift traffic gradually by country and cohort. Keep the monolith catalogue/search route live until parity and peak tests pass.\n- Introduce cache policies, invalidation events, stale-data limits, and cache-bypass operational controls.\n- Do not make the new search service authoritative for price or stock. It displays explicitly versioned read models from their owning domains.", "dependencies": ["S7", "S8", "S9", "S10"]}, {"step_id": "S12", "title": "Modernise inventory integration and availability reads", "description": "Separate warehouse file exchange from customer-facing inventory reads while preserving warehouse and order-system correctness. Inventory changes are operationally sensitive and require explicit freshness semantics.\n\n- Build a warehouse integration adapter that validates, records, deduplicates, and acknowledges inbound and outbound files without changing warehouse contracts initially.\n- Publish inventory-change events and create an availability read model for storefront and search use.\n- Define country and fulfilment-node stock semantics, safety-stock rules, oversell tolerance, freshness targets, and customer messaging for stale or unavailable stock.\n- Shadow-compare the new availability result with the monolith for all products and warehouses. Reconcile every discrepancy before traffic expansion.\n- Preserve current monolith stock reservation and allocation authority until order and inventory ownership boundaries are fully designed.\n- Provide an immediate fallback to monolith availability reads and a replayable file-processing recovery process.", "dependencies": ["S7", "S8", "S9", "S10"]}, {"step_id": "S13", "title": "Discover and contain pricing and promotions", "description": "Treat pricing and promotions as the highest-risk business capability. First make its behaviour observable and testable; do not attempt a big-bang rewrite based on incomplete knowledge.\n\n- Form a dedicated cross-functional squad with senior engineers, merchandising, finance, country representatives, customer support, and QA.\n- Inventory all rules, stored procedures, configuration tables, overrides, scheduled jobs, manual back-office actions, and country-specific exceptions.\n- Capture real production decision inputs and outputs into a privacy-safe decision log. Build a golden-master corpus covering products, customer segments, countries, currencies, dates, carts, vouchers, stacking, tax, and edge cases.\n- Put the existing engine behind a versioned pricing façade. New callers use the façade even while it delegates to monolith logic.\n- Build a new rules-evaluation candidate service only for well-understood rule slices. Shadow-evaluate and compare exact price, discount, explanation, and latency before any customer exposure.\n- Require business sign-off, discrepancy classification, and financial-impact analysis before moving each rule slice. Keep a per-slice route-back switch to the legacy engine.", "dependencies": ["S2", "S6", "S7", "S8", "S9", "S10"]}, {"step_id": "S14", "title": "Extract customer and loyalty capabilities safely", "description": "Move customer-facing identity-adjacent data only after privacy, consent, and data ownership are clear. Avoid introducing inconsistent account state across countries and channels.\n\n- Define the canonical customer identifier, consent model, data-retention rules, subject-access and deletion workflows, and access-control model.\n- Start with a replicated customer profile read service, then migrate bounded profile writes through a façade with idempotency and audit trails.\n- Move loyalty functions in small slices, such as balance inquiry before accrual or redemption, using a ledger model and reconciliation against legacy balances.\n- Support account-session compatibility across web, mobile, monolith, and new services throughout the transition.\n- Reconcile customer records, consent states, and loyalty balances daily during migration. Route exceptions to trained operations staff.\n- Retain a compatibility adapter for legacy back-office functions until those workflows are migrated or retired.", "dependencies": ["S7", "S8", "S9", "S10"]}, {"step_id": "S15", "title": "Extract order views and bounded post-order workflows", "description": "Create independently deployable order-related value without prematurely splitting the transactional checkout path. Start with event-driven reads and post-order processes that can tolerate asynchronous integration.\n\n- Publish reliable order lifecycle events from the monolith using the outbox pattern.\n- Build an order query service for customer-service, customer self-service, notifications, and selected back-office views. Validate it against monolith order history and live state.\n- Extract bounded workflows such as notifications, selected return initiation, return-status tracking, and non-financial order enrichment where ownership is explicit.\n- Preserve monolith order creation, payment capture coordination, cancellation authority, refund authority, and warehouse order export until their transition design is approved.\n- Implement reconciliation for order counts, states, refunds, returns, notification delivery, and event lag.\n- Ensure every new order-facing view identifies source freshness and has a monolith fallback for support staff.", "dependencies": ["S7", "S8", "S9", "S10", "S14"]}, {"step_id": "S16", "title": "Create cart, checkout, and payment transition architecture", "description": "Prepare the revenue-critical transactional path through façade-first migration, exhaustive provider testing, and progressive traffic control. This stage must not force immediate service ownership transfer.\n\n- Define cart identity, guest-to-account merge rules, session persistence, currency and country transitions, promotion snapshots, inventory checks, and checkout idempotency keys.\n- Introduce a checkout façade that initially delegates to the monolith. Route storefront and mobile gradually while maintaining response and error compatibility.\n- Isolate each payment provider behind versioned adapters with token handling, webhook verification, idempotent authorisation and capture, retry policy, reconciliation, and provider-specific fallback behaviour.\n- Build a payment ledger and daily reconciliation process covering authorisations, captures, refunds, chargebacks, provider settlements, and orders.\n- Shadow-run checkout orchestration and payment-adapter decisions where possible. Use provider test environments and controlled internal cohorts before customer traffic.\n- Do not split the final order-creation transaction until failure-mode analysis, compensating actions, support procedures, and sale-peak load tests demonstrate acceptable risk.", "dependencies": ["S7", "S8", "S9", "S10", "S11", "S12", "S13", "S15"]}, {"step_id": "S17", "title": "Transfer ownership through controlled data cutovers", "description": "Move write ownership one entity group at a time after services have proven read parity and operational maturity. Each cutover is a reversible state transition, not a one-time database migration.\n\n- For each entity, document source of truth, writer cutover sequence, replication direction, API consumers, data-retention obligations, reconciliation rules, and rollback point.\n- Use expand-contract schemas, backfills with checksums, change capture or outbox replication, dual-read validation, and carefully bounded write cutovers.\n- Avoid unrestricted dual writes. During transition, route writes through one command owner that publishes changes reliably to dependent systems.\n- Reconcile continuously by identifiers, row counts, hashes, financial totals, stock totals, and business state transitions. Define thresholds that automatically halt traffic expansion.\n- Retain legacy data read access and compatibility APIs until all consumers are migrated and the defined observation period has passed.\n- Schedule any high-risk ownership cutover outside sales protection windows, with an approved rollback rehearsal and staffed hypercare period.", "dependencies": ["S7", "S8", "S11", "S12", "S13", "S14", "S15", "S16"]}, {"step_id": "S18", "title": "Execute progressive traffic migration and rollback drills", "description": "Move production traffic only through measured, reversible increments. Every migration uses the same operational playbook regardless of domain.\n\n- Progress through dark launch, shadow comparison, employee cohort, low-risk country or cohort, 1%, 5%, 25%, 50%, and full traffic stages where appropriate.\n- Define quantitative promotion criteria for each stage: error rate, latency, conversion, search quality, price parity, payment approval rate, order completion, inventory discrepancy, support contacts, and reconciliation lag.\n- Automate route rollback and validate it with game days. Rollback must restore a known compatible route without data loss or customer-visible duplicate operations.\n- Run failure injection for dependency loss, event delay, duplicate messages, provider timeout, cache failure, database failover, and warehouse-file replay.\n- Maintain staffed hypercare after each material expansion, with business, support, and engineering representatives able to pause or reverse rollout.\n- Freeze traffic increases before sales protection windows. Use those windows only for monitoring, capacity verification, defect fixes with approved exceptions, and rehearsed rollback readiness.", "dependencies": ["S4", "S8", "S10", "S11", "S12", "S13", "S14", "S15", "S16", "S17"]}, {"step_id": "S19", "title": "Prepare peak-season resilience and capacity certification", "description": "Certify both the hybrid estate and fallback paths for January and July sales. A service is not production-ready if its rollback target cannot sustain the traffic it might receive.\n\n- Forecast peak demand by country, channel, page type, checkout step, payment provider, product launch, and warehouse activity.\n- Load-test the full hybrid path at at least 12x observed baseline plus agreed headroom, including gateway, caches, monolith, services, event platform, databases, search, payment adapters, and warehouse integration.\n- Test traffic reversion from each service to monolith and confirm that the monolith, database, and legacy search can absorb the reverted load.\n- Establish capacity reservations, autoscaling limits, database connection limits, cache warm-up procedures, provider rate-limit agreements, and operational staffing plans.\n- Conduct sale-day simulations, incident command exercises, communications rehearsals, and business-continuity tests with payment providers and warehouse stakeholders.\n- Obtain formal peak-readiness sign-off from engineering, operations, commerce, finance, warehouse, and customer support.", "dependencies": ["S4", "S5", "S8", "S11", "S12", "S13", "S16", "S18"]}, {"step_id": "S20", "title": "Retire legacy paths and establish steady-state service governance", "description": "Conclude the 12-month programme by removing only proven-obsolete paths and making service ownership sustainable. Retain legacy components where removal would create unjustified business risk.\n\n- Decommission monolith endpoints, batch jobs, Lucene components, table access paths, and stored procedures only after consumer inventory, data archival, reconciliation, and rollback-retention periods are complete.\n- Remove temporary replication and compatibility adapters in controlled releases. Update runbooks, diagrams, recovery procedures, and ownership records at the same time.\n- Measure and reduce residual monolith coupling, direct database access, synchronous dependency chains, event lag, and operational toil.\n- Establish quarterly architecture reviews, API and event lifecycle governance, service scorecards, dependency upgrade policies, resilience testing, and disaster-recovery exercises.\n- Conduct a post-migration review against business outcomes, incident history, delivery lead time, cost, and peak performance. Prioritise any remaining pricing, checkout, order, or database decomposition work as a funded follow-on roadmap.\n- Confirm that independently deployable services have named owners, on-call coverage, SLOs, operational documentation, and tested rollback or recovery procedures.", "dependencies": ["S17", "S18", "S19"]}], "estimated_complexity": "high", "success_metrics": "- No unplanned customer-facing downtime is attributable to migration work during the 12-month programme.\n- Every production migration has a documented, rehearsed rollback that can be initiated within 15 minutes and preserves financial, order, payment, and inventory integrity.\n- January and July sales periods complete with at least the pre-migration peak availability, conversion rate, payment approval rate, and order throughput.\n- The hybrid platform sustains at least 12x observed normal load plus agreed headroom in full-path load and failover tests before each sales period.\n- Critical journeys achieve at least 95% automated API, integration, contract, and end-to-end regression coverage by business-risk weighting, with 100% coverage of defined checkout, payment, order, stock, refund, and price-parity scenarios.\n- Catalogue/search, inventory availability, customer/loyalty slices, order query/post-order slices, and selected checkout/payment façade capabilities are independently deployable with named ownership, SLOs, dashboards, runbooks, and on-call support.\n- All extracted services have zero direct writes to another service's database, and all cross-service state propagation uses governed APIs or versioned events.\n- For each migrated entity group, reconciliation identifies less than 0.01% unresolved record discrepancies and zero unresolved financial discrepancies at cutover completion.\n- Pricing and promotion decision parity for any migrated rule slice is at least 99.99% against approved golden-master cases, with all remaining differences explicitly approved by business owners.\n- Deployment frequency for independently deployable services reaches at least weekly, with no mandatory monolith maintenance window required for routine compatible releases.\n- Mean time to detect critical customer-journey failures is below 5 minutes, and mean time to restore or roll back migration-related severity-one incidents is below 30 minutes.\n- Feature delivery continues throughout the programme, with planned business roadmap throughput maintained at no less than 80% of the agreed baseline."}

[SYSTEM]
You are an expert assistant in complex project planning.
Your task is to generate a detailed and structured action plan to reach the main objective in the most professional and most detailed way, no matter how much work or steps will be needed to perform.
Use your internal reasoning processes to think deeply about the problem and create the most comprehensive plan possible.
Take as much time and space as you need to think through all aspects of the problem.
After your thorough analysis, answer with the plan in the requested structure.

Every text field you write will be read by a busy person, so write for them: short sentences, one idea each, in short paragraphs. Text fields accept Markdown: separate paragraphs with a blank line, use a bullet list when you enumerate things and plain paragraphs when you explain or argue, and bold at most one key phrase per paragraph. A text of more than three sentences must be split into paragraphs; never deliver a single unbroken block of text.

[HUMAN]
Main Objective: "A mid-size European fashion retailer runs its e-commerce on a 10-year-old monolith: Java 8 / Spring, about 2 million lines, one PostgreSQL database of 1.2 TB with 350 tables and heavy use of stored procedures and cross-module joins. It serves 8 countries, 3 currencies and 4 languages, with about 40,000 orders a day and peaks of 12x during sales. The monolith contains: the storefront (server-rendered, plus a separate mobile app hitting the same endpoints), catalogue and search (a Lucene index rebuilt nightly), pricing and promotions (the most complex module, 200,000 lines, with country-specific rules nobody fully understands), cart and checkout with three payment providers, order management, inventory synchronised every 15 minutes with the warehouse system via file exchange, customer accounts and loyalty, returns, and a back-office used by 300 staff. Deployments happen every two weeks as a single artefact with a 30-minute maintenance window; test coverage is 25% and mostly unit tests. Five teams of 8 developers, each organised around a business area but all committing to the same repository.

Plan the migration to independently deployable services over 12 months with no feature freeze, no unplanned downtime and the ability to roll back every step, preserving the peak-season capacity (sales in January and July must not be put at risk)."

Generate an initial, detailed, and well-structured plan proposal.

The plan has these parts:
- "steps": the list of steps, each with "step_id" (a short label unique within this proposal, such as S1, S2, S3), "title" (a short, descriptive title), "description" (what the step does and how, in Markdown: a short opening paragraph and then, if there are several concrete things to say, a bullet list) and "dependencies" (the step_ids of the other steps of THIS proposal that must be completed first; empty if none).
- "estimated_complexity": "low", "medium" or "high".
- "success_metrics": clear and measurable success metrics, one per line as a bullet list.

Think deeply about the problem, considering all aspects and implications. Develop a comprehensive understanding of what needs to be done to achieve the objective, then produce the plan.

{"steps": [{"step_id": "S1", "title": "Charter, governance and non-negotiables", "description": "Write a short **migration charter** that product, ops, finance and all five teams sign.\n\nFeature work never stops. Only production risk is constrained.\n\n- Name one accountable migration lead and a weekly steering forum.\n- Ban big-bang rewrites, shared-database-first splits and un-reversible cutovers.\n- Require a tested rollback for every production step.\n- Keep the two-week monolith release train for features until a domain is fully extracted.", "dependencies": []}, {"step_id": "S2", "title": "Peak calendar and freeze protocol", "description": "Protect **January and July** sales with hard engineering blackouts.\n\nNo extractions, schema splits or traffic switches in the six weeks before a sale or the two weeks after, unless they are already proven and idle.\n\n- Publish the 12-month calendar in week one.\n- Freeze means no new migration risk, not a feature freeze.\n- Require a peak capacity rehearsal before each blackout.\n- Give ops a veto on any change that could affect checkout, payments, stock or search.", "dependencies": ["S1"]}, {"step_id": "S3", "title": "Baseline architecture, data and SLOs", "description": "Measure the live system before changing it.\n\nBuild a factual map of the 2M-line monolith, the 1.2 TB database and the real traffic shape.\n\n- Trace the top 30 user journeys and the 350 tables they touch.\n- Record p50/p95/p99, error rates and 12x peak headroom per journey.\n- Inventory stored procedures, cross-module joins and file exchanges.\n- Tag every endpoint used by the storefront, mobile app and back-office.", "dependencies": ["S1"]}, {"step_id": "S4", "title": "Delivery platform, flags and progressive delivery", "description": "Give every team a **safe way to ship** without the 30-minute maintenance window.\n\nNew work deploys behind flags. Old work stays on the existing train until it is ready.\n\n- Add feature flags, weighted routing and instant revert at the edge.\n- Build CI that can later publish one artefact per service.\n- Keep Java 8 on the monolith. Start new services on a current LTS.\n- Provide preview environments that replay production-like traffic.", "dependencies": ["S1"]}, {"step_id": "S5", "title": "Observability and error budgets", "description": "Instrument the monolith as if it were already many services.\n\nYou cannot extract what you cannot see.\n\n- Add distributed tracing, RED metrics and structured logs with correlation IDs.\n- Define SLOs for search, PDP, cart, checkout, payments and back-office.\n- Page on error-budget burn, not on CPU.\n- Dashboards must show monolith vs new service side by side for every cutover.", "dependencies": ["S3", "S4"]}, {"step_id": "S6", "title": "Safety net: journeys, contracts and load", "description": "Raise the net where extraction will cut.\n\nUnit coverage at 25% is not enough. Protect behaviour, not lines.\n\n- Record golden journeys for browse, price, cart, checkout, order, return and loyalty.\n- Add contract tests on every mobile and storefront endpoint.\n- Capture characterization tests around stored procedures before moving them.\n- Automate a 12x peak load test and run it before each sale and each major cutover.", "dependencies": ["S3"]}, {"step_id": "S7", "title": "Bounded contexts and extraction backlog", "description": "Draw domain boundaries from the business, not from the package tree.\n\nSequence work by **risk and coupling**, not by fashion.\n\n- Contexts: identity, catalogue, search, pricing, inventory, cart, checkout, orders, returns, loyalty, back-office.\n- Extract read-mostly and already-async seams first (search, inventory files).\n- Leave pricing and checkout until dual-run and reconciliation exist.\n- Rank a 12-month backlog with a rollback story on every item.", "dependencies": ["S3"]}, {"step_id": "S8", "title": "Team operating model without a freeze", "description": "Keep five domain teams. Stop treating the repo as a single ownership blob.\n\nEach team ships features in the monolith **and** prepares its future service.\n\n- Assign a service to own per team, plus a shared platform pair.\n- Code owners and module walls inside the current repository first.\n- A small platform group owns gateway, flags, events, CI and data tooling.\n- Product still plans features; migration work is a percentage of each sprint, not a separate freeze.", "dependencies": ["S1", "S7"]}, {"step_id": "S9", "title": "Modularise the monolith in place", "description": "Create seams before you create processes.\n\nNew code may not add cross-module joins or new stored-procedure coupling.\n\n- Split packages by bounded context with compile-time walls.\n- Replace in-process calls at boundaries with interfaces (branch by abstraction).\n- Document and freeze the worst pricing and checkout internals; wrap them.\n- Ban new features from reaching into another team's tables.", "dependencies": ["S6", "S7"]}, {"step_id": "S10", "title": "Strangler facade and instant traffic rollback", "description": "Put a reverse proxy in front of every public and mobile endpoint.\n\nClients keep the same URLs. You choose monolith or service per route and per percentage.\n\n- Preserve headers, sessions, cookies and the four languages.\n- Shadow traffic before any live percentage.\n- Rollback is a route change, not a redeploy, and must complete in minutes.\n- Storefront SSR and the mobile app stay compatible until a later BFF if needed.", "dependencies": ["S4", "S5"]}, {"step_id": "S11", "title": "Events, outbox and CDC backbone", "description": "Give the monolith a **reversible integration spine**.\n\nServices must not call each other's databases. They subscribe to facts.\n\n- Add an outbox in the same Postgres transaction as business writes.\n- CDC from the monolith for tables you do not yet own.\n- Standard event names for product, price, stock, customer, order and return.\n- Idempotent consumers and a dead-letter process before the first extraction.", "dependencies": ["S5", "S9"]}, {"step_id": "S12", "title": "Data-change playbook: dual-write, reconcile, roll back", "description": "Treat every data move as a campaign with an abort switch.\n\nThe 1.2 TB database stays the system of record until a service proves otherwise.\n\n- Dual-write with the monolith write winning on conflict during trial.\n- Nightly and continuous reconciliation with row-level diffs.\n- Never cut stored procedures until logic has an equivalent test harness.\n- Rollback means stop writes to the new store and keep serving from Postgres.", "dependencies": ["S11"]}, {"step_id": "S13", "title": "Extract search as the first service", "description": "Replace the nightly Lucene rebuild with an independently deployed **search service**.\n\nThis is read-heavy, already eventually consistent, and off the payment path.\n\n- Index from catalogue and price events, not from a nightly dump.\n- Shadow queries against current Lucene until precision/recall match.\n- Shift traffic 1% → 10% → 50% → 100% with instant route rollback.\n- Keep the old index warm through the next sale as a cold standby.", "dependencies": ["S2", "S8", "S10", "S11", "S12"]}, {"step_id": "S14", "title": "Extract catalogue read models", "description": "Serve product, media and localisation from a catalogue service.\n\nWrites can stay in the monolith until editors have a new path.\n\n- Build country and language-specific read models for eight markets.\n- Keep one product identity so pricing, stock and search stay aligned.\n- Cut storefront and mobile read traffic via the strangler.\n- Do not move merchandising tools until reads are stable.", "dependencies": ["S13"]}, {"step_id": "S15", "title": "Extract identity, accounts and session", "description": "Pull login, profile, addresses and session behind a dedicated service.\n\nMobile and web keep the same auth cookies or tokens during the switch.\n\n- Migrate sessions without forced logouts.\n- Dual-read loyalty points until that domain is extracted.\n- GDPR/export and deletion flows must work in both systems.\n- Rollback restores monolith auth with no password resets.", "dependencies": ["S8", "S10", "S12"]}, {"step_id": "S16", "title": "Extract inventory and warehouse sync", "description": "Replace the 15-minute file exchange with an inventory service that still talks to the warehouse.\n\nThe warehouse interface stays file-based until they can change. Your side becomes events.\n\n- Service owns ATP, reservations and oversell rules.\n- Adapter keeps the existing file contract so warehouse risk is zero.\n- Cart and checkout read stock from the service via API or replica.\n- Prove no extra oversell versus today's 15-minute lag before a sale.", "dependencies": ["S8", "S11", "S12"]}, {"step_id": "S17", "title": "Pricing archaeology and dual-run harness", "description": "Do not extract the 200k-line pricing module until you can prove equivalence.\n\nNobody fully understands country rules. Tests must become the spec.\n\n- Capture production price traces for all eight countries and three currencies.\n- Build a harness that replays promotions, baskets and edge SKUs.\n- Freeze behavioural snapshots; new promo features implement twice until cutover.\n- Only then wrap pricing behind an interface inside the monolith.", "dependencies": ["S6", "S9"]}, {"step_id": "S18", "title": "Extract pricing and promotions behind dual-run", "description": "Run the new pricing service in **shadow** until it matches the monolith on live baskets.\n\nCheckout keeps using monolith prices until the error budget is clean.\n\n- Compare every quote; alert on any currency, tax or promo mismatch.\n- Shift read traffic first, then write of promo usage.\n- Keep the monolith engine deployable as rollback through the next two sales.\n- Country-specific rules move last, one market at a time if needed.", "dependencies": ["S14", "S17", "S12"]}, {"step_id": "S19", "title": "Extract cart", "description": "Move the cart after identity, catalogue, stock and price reads are stable.\n\nCart is stateful. Lose no baskets during cutover.\n\n- Dual-write carts; reconcile abandoned and active baskets.\n- Preserve promo application using the dual-run price API.\n- Session migration must survive app versions in the wild.\n- Rollback reattaches baskets to the monolith cart tables.", "dependencies": ["S15", "S16", "S18"]}, {"step_id": "S20", "title": "Extract checkout and payment orchestration", "description": "Strangle checkout without touching the three payment providers in one step.\n\nA thin orchestration service talks to existing provider integrations first.\n\n- Keep PCI and provider contracts stable; wrap, do not rewrite.\n- Idempotent order placement with an outbox to OMS.\n- Canary by country and by payment method.\n- Rollback is route-plus-flag; in-flight payments complete on the old path.", "dependencies": ["S19"]}, {"step_id": "S21", "title": "Extract order management", "description": "Move post-purchase order state once checkout emits reliable events.\n\nOMS must survive 12x peaks and warehouse files.\n\n- Order of record shifts only after reconciliation is clean for a full weekly cycle.\n- Back-office screens can still read a projection while writes move.\n- Returns and finance reports stay correct during dual-run.\n- Keep monolith OMS as standby through one sale after cutover.", "dependencies": ["S20"]}, {"step_id": "S22", "title": "Extract returns, loyalty and remaining back-office", "description": "Peel remaining domains once orders and identity are independent.\n\nStaff of 300 must not get a big-bang UI change.\n\n- Returns service consumes order events and drives refunds via payment facade.\n- Loyalty becomes the owner of points with dual-write from checkout.\n- Back-office gets BFFs or modular UIs per domain, not a new monolith.\n- Train staff per screen group; keep old screens until the new ones match.", "dependencies": ["S15", "S21"]}, {"step_id": "S23", "title": "Split data ownership and retire stored procedures", "description": "Give each stable service its **own schema or database** only after traffic and reconciliation are boring.\n\nShared Postgres is allowed during transition. It is not the end state.\n\n- Move table ownership context by context; no cross-service joins.\n- Rewrite stored procedures into service code with the characterization harness.\n- Shrink the 1.2 TB monolith database as tables go dark.\n- Rollback is restoring replication to the monolith copy, practised in staging.", "dependencies": ["S16", "S18", "S21"]}, {"step_id": "S24", "title": "Independent deploy pipelines and repository split", "description": "When a service is independently releasable, stop bundling it into the fortnightly artefact.\n\nTeams ship on demand with automated checks and progressive delivery.\n\n- One pipeline per service: test, canary, promote, revert.\n- Split repos only after module walls and CI already work in the monorepo.\n- Contract tests gate consumer and provider deploys.\n- The remaining monolith keeps the old two-week train until it is small.", "dependencies": ["S8", "S23"]}, {"step_id": "S25", "title": "Peak rehearsals, chaos and residual shrink", "description": "Prove **12x capacity** on the mixed architecture before each sale.\n\nThen keep shrinking the monolith so it is a shell, not a risk.\n\n- Game-days: provider failure, CDC lag, flag revert, search fallback, stock file delay.\n- Scale tests on checkout, search and inventory with production-sized data.\n- Delete dead modules, jobs and tables only after two quiet weeks.\n- End state: independently deployable services, instant rollback still in place, no unplanned downtime attributed to the programme.", "dependencies": ["S2", "S22", "S24"]}], "estimated_complexity": "high", "success_metrics": "- Zero unplanned downtime attributed to migration work across the 12 months.\n- Every production cutover has a practised rollback that restores the previous path in minutes.\n- January and July peak capacity at or above today's 12x headroom, with no extra error-budget burn on search, cart, checkout or payments.\n- Feature throughput stays at or above the current two-week train; no programme-wide feature freeze.\n- At least search, catalogue, identity, inventory, pricing, cart, checkout and OMS deploy independently of the monolith artefact.\n- Dual-run mismatch rate for prices and stock below an agreed threshold before each traffic shift (target: 0 on money paths).\n- Golden-journey pass rate 100% on critical paths before and after each cutover.\n- Monolith database coupling reduced: no new cross-context joins; stored-procedure call volume on extracted domains at zero after ownership transfer.\n- Mean time to revert a bad service release under 10 minutes via flags or routing."}

[SYSTEM]
You are an expert assistant in complex project planning.
Your task is to generate a detailed and structured action plan to reach the main objective in the most professional and most detailed way, no matter how much work or steps will be needed to perform.
Use your internal reasoning processes to think deeply about the problem and create the most comprehensive plan possible.
Take as much time and space as you need to think through all aspects of the problem.
After your thorough analysis, answer with the plan in the requested structure.

Every text field you write will be read by a busy person, so write for them: short sentences, one idea each, in short paragraphs. Text fields accept Markdown: separate paragraphs with a blank line, use a bullet list when you enumerate things and plain paragraphs when you explain or argue, and bold at most one key phrase per paragraph. A text of more than three sentences must be split into paragraphs; never deliver a single unbroken block of text.

[HUMAN]
Main Objective: "A mid-size European fashion retailer runs its e-commerce on a 10-year-old monolith: Java 8 / Spring, about 2 million lines, one PostgreSQL database of 1.2 TB with 350 tables and heavy use of stored procedures and cross-module joins. It serves 8 countries, 3 currencies and 4 languages, with about 40,000 orders a day and peaks of 12x during sales. The monolith contains: the storefront (server-rendered, plus a separate mobile app hitting the same endpoints), catalogue and search (a Lucene index rebuilt nightly), pricing and promotions (the most complex module, 200,000 lines, with country-specific rules nobody fully understands), cart and checkout with three payment providers, order management, inventory synchronised every 15 minutes with the warehouse system via file exchange, customer accounts and loyalty, returns, and a back-office used by 300 staff. Deployments happen every two weeks as a single artefact with a 30-minute maintenance window; test coverage is 25% and mostly unit tests. Five teams of 8 developers, each organised around a business area but all committing to the same repository.

Plan the migration to independently deployable services over 12 months with no feature freeze, no unplanned downtime and the ability to roll back every step, preserving the peak-season capacity (sales in January and July must not be put at risk)."

Generate an initial, detailed, and well-structured plan proposal.

The plan has these parts:
- "steps": the list of steps, each with "step_id" (a short label unique within this proposal, such as S1, S2, S3), "title" (a short, descriptive title), "description" (what the step does and how, in Markdown: a short opening paragraph and then, if there are several concrete things to say, a bullet list) and "dependencies" (the step_ids of the other steps of THIS proposal that must be completed first; empty if none).
- "estimated_complexity": "low", "medium" or "high".
- "success_metrics": clear and measurable success metrics, one per line as a bullet list.

Think deeply about the problem, considering all aspects and implications. Develop a comprehensive understanding of what needs to be done to achieve the objective, then produce the plan.

Answer with a single JSON object, and nothing else, that conforms to this JSON schema:
{"description": "The structure every proposing agent must answer with.", "properties": {"steps": {"items": {"description": "One step of a proposed plan. Dependencies are step_ids of the same proposal.", "properties": {"step_id": {"title": "Step Id", "type": "string"}, "title": {"title": "Title", "type": "string"}, "description": {"title": "Description", "type": "string"}, "dependencies": {"items": {"type": "string"}, "title": "Dependencies", "type": "array"}}, "required": ["step_id", "title", "description", "dependencies"], "title": "Step", "type": "object", "additionalProperties": false}, "title": "Steps", "type": "array"}, "estimated_complexity": {"title": "Estimated Complexity", "type": "string"}, "success_metrics": {"title": "Success Metrics", "type": "string"}}, "required": ["steps", "estimated_complexity", "success_metrics"], "title": "ProposalContent", "type": "object", "additionalProperties": false}

{"steps": [{"step_id": "S1", "title": "Baseline and decompose the monolith into bounded contexts", "description": "Capture the current behaviour, data model, and operational risks before changing anything. The output is a shared map that justifies every later cutover.\n- Inventory all modules, endpoints, database tables, stored procedures, cross-module joins, external integrations, and batch jobs.\n- Map business capabilities to bounded contexts and identify candidate service seams and data owners.\n- Record every country/currency/language variation, especially the 200k-line pricing and promotions module.\n- Capture the peak-season calendar, current deployment windows, known failure modes, and rollback mechanisms.\n- Create a risk register with blast radius and rollback criteria for each candidate extraction.", "dependencies": []}, {"step_id": "S2", "title": "Define target service architecture and migration sequence", "description": "Agree the target state and the guardrails before building any new service.\n- Publish target decomposition: storefront, catalogue/search, pricing/promotions, cart/checkout, orders, inventory, customers/loyalty, returns, back-office.\n- Define synchronous APIs, asynchronous events, idempotency, retries, sagas, and eventual consistency where required.\n- Define data ownership and database-per-service strategy; prohibit cross-service joins and direct access to another service's tables.\n- Define API versioning, security, tenancy, and country-specific routing.\n- Choose migration sequence: start with low-risk read-heavy capabilities and delay peak-sensitive cutovers until outside sales windows.\n- Set the rollback requirement: every change must be behind a flag or reversible migration with rehearsed rollback.", "dependencies": ["S1"]}, {"step_id": "S3", "title": "Establish observability, SLOs and production load testing", "description": "Make the current system measurable so cutovers are based on data, not hope.\n- Add structured logs, metrics, and distributed tracing to the monolith and future services.\n- Define SLOs and error budgets for storefront, catalogue, cart, checkout, payments, and order management.\n- Add synthetic transactions and real-user monitoring for 8 countries, 3 currencies, and 4 languages.\n- Build a performance test environment that replays production-like traffic at peak 12x volume.\n- Create dashboards for golden signals, slow queries, stored procedure hotspots, and cache/index health.", "dependencies": ["S1"]}, {"step_id": "S4", "title": "Build zero-downtime CI/CD and database migration automation", "description": "This is the safety rail for every later step: frequent, reversible, low-risk deployments.\n- Replace the biweekly single-artifact release with a pipeline supporting per-service builds, automated tests, security scans, and deployment.\n- Introduce canary and blue-green deployment with automated rollback based on SLOs and error budgets.\n- Add expand/contract database migration patterns: first add new schema, dual-write or synchronise, switch reads, then remove old schema in a later release.\n- Ensure every service change is independently deployable in minutes, with no planned maintenance window.\n- Use infrastructure-as-code and immutable artifacts for all environments.", "dependencies": ["S2"]}, {"step_id": "S5", "title": "Strengthen tests and add contract testing before cutting seams", "description": "Raise confidence in behaviour without freezing features, focusing on seams to be extracted.\n- Build characterization tests around current API endpoints, stored procedures, pricing rules, and checkout flows.\n- Add consumer-driven contract tests between the monolith and new services.\n- Introduce mutation testing and enforce at least 80% coverage on changed code.\n- Add data-migration tests, reconciliation tests, and performance regression gates to CI/CD.\n- Keep a long-running dual-read and diff harness for later services.", "dependencies": ["S3", "S4"]}, {"step_id": "S6", "title": "Introduce traffic routing and feature flag platform", "description": "Enable gradual migration and instant rollback without redeploying the entire monolith.\n- Deploy a feature flag system and edge/API gateway that can route traffic by customer, country, currency, language, percentage, and header.\n- Add dark-launch capability to send shadow traffic to new services while the monolith remains source of truth.\n- Implement kill switches that revert to monolith paths in one action.\n- Integrate flags with SLO dashboards and deployment rollback.", "dependencies": ["S4", "S5"]}, {"step_id": "S7", "title": "Extract customer accounts and loyalty as pilot service", "description": "Prove the extraction playbook on a well-bounded, lower-risk capability before touching the most complex modules.\n- Create a customer service owning customer, address, and loyalty data; expose a REST API with the same contracts.\n- Move related monolith code behind an anti-corruption layer; run dual-writes or CDC to keep data in sync.\n- Use expand/contract database migration: retain monolith tables temporarily, synchronise with the service, then switch reads/writes by flag.\n- Launch to a small country and a small traffic percentage; monitor SLOs and rollback if errors exceed the error budget.\n- Use the pilot to refine templates, runbooks, and training for other teams.", "dependencies": ["S2", "S3", "S4", "S5", "S6"]}, {"step_id": "S8", "title": "Extract catalogue and search into a dedicated service", "description": "Move the read-heavy catalogue and search path first, as it is valuable and relatively safe if done in shadow mode.\n- Build a catalogue/search service that owns product, category, and search data; maintain the Lucene index within the service or via a dedicated index.\n- Synchronise catalogue data from the monolith through CDC or events; stop cross-module joins.\n- Serve storefront and mobile via the new catalogue/search API; run shadow reads against the monolith and compare.\n- Route reads progressively by country and language and validate search quality, latency, and conversion.\n- Keep the monolith fallback and flag-based rollback until after the peak readiness gate.", "dependencies": ["S3", "S4", "S5", "S6", "S7"]}, {"step_id": "S9", "title": "Extract pricing and promotions with dual-run comparison", "description": "The most complex module; migration must be based on observed behavioural equivalence.\n- Build a pricing/promotions service with country-specific rules as versioned configuration or domain rules.\n- Run the new service in shadow mode on all checkout/cart/catalogue calls and compare every calculation with the monolith for months before cutover.\n- Treat any divergence as a defect; require 100% parity on sampled and historical promotion scenarios before routing live traffic.\n- Expose a pricing API and route live reads/writes only by country and promotion type, with immediate rollback.\n- Keep the monolith promotion engine available until after all peak seasons.", "dependencies": ["S7", "S8"]}, {"step_id": "S10", "title": "Extract inventory service and modernise warehouse integration", "description": "Replace the 15-minute file exchange with safer, event-driven inventory updates while keeping the old path as fallback.\n- Build an inventory service owning stock levels, reservations, and warehouse sync logic.\n- Integrate with the warehouse system via API or events and keep the file exchange running in parallel for dual sync.\n- Expose inventory availability and reservation APIs for cart, checkout, and back-office.\n- Run reconciliation between the old file batch and the new event flow for all SKUs; eliminate divergence before cutover.\n- Route inventory consumers to the service progressively, maintaining the monolith fallback.", "dependencies": ["S7"]}, {"step_id": "S11", "title": "Extract cart and checkout service", "description": "Move the highest-value transaction path only after its dependencies are available and proven.\n- Build a cart/checkout service that owns cart state and checkout workflow; it calls customer, catalogue, pricing, and inventory APIs with fallbacks.\n- Integrate the three payment providers through adapters; implement idempotency, retries, and reconciliation.\n- Use saga or orchestration for payment, inventory reservation, and order creation.\n- Route by country, currency, and traffic percentage; start with one payment provider and one country.\n- Rehearse rollback to monolith checkout and validate that no cart or payment is lost.", "dependencies": ["S7", "S8", "S9", "S10"]}, {"step_id": "S12", "title": "Peak readiness gate before first sales peak", "description": "Protect the first peak by freezing risky cutovers while allowing normal feature work through flags.\n- Freeze new service cutovers and irreversible data migrations for four weeks before and during the peak.\n- Run production-like load tests at 12x baseline with monolith and new services in their current routing ratios.\n- Rehearse rollback for every extracted service and confirm the monolith fallback handles full load.\n- Pre-scale infrastructure to at least 30% above expected peak.\n- Keep on-call and war-room runbooks ready; certify only if all SLOs pass in load tests.", "dependencies": ["S7", "S8", "S9", "S10", "S11"]}, {"step_id": "S13", "title": "Extract order management service after first peak", "description": "Move order persistence and lifecycle after the first peak, using events from checkout and inventory.\n- Build an order service owning orders and order lines; consume order-placed events from checkout and payment.\n- Replace monolith order creation and status update code behind flags.\n- Backfill historical orders into the service and run reconciliation.\n- Route order read/write traffic progressively; maintain the monolith fallback.\n- Ensure returns and customer service integration remains consistent.", "dependencies": ["S11", "S12"]}, {"step_id": "S14", "title": "Extract returns service", "description": "Move returns and refunds out of the monolith once order and inventory services are stable.\n- Build a returns service owning return requests, labels, refund settlements, and status.\n- Integrate with order, inventory, and payment services via APIs and events.\n- Migrate business rules country-by-country with dual-run comparison.\n- Keep the monolith fallback and rollback for all return journeys.", "dependencies": ["S13"]}, {"step_id": "S15", "title": "Extract back-office capabilities", "description": "Decouple internal staff tools from the monolith so operations run on the new services.\n- Build a new back-office UI or modular back-office services that use the same APIs as storefront services.\n- Migrate catalogue, pricing, inventory, order, customer, and returns management screens incrementally.\n- Route the 300 staff users per function and country; keep the old monolith back-office available during transition.\n- Automate manual processes and remove direct SQL access to migrated data.", "dependencies": ["S13", "S14"]}, {"step_id": "S16", "title": "Peak readiness gate before second sales peak", "description": "Repeat the readiness gate for the second peak with more services in production.\n- Freeze cutovers and irreversible migrations four weeks before and during the peak.\n- Run full production-like load test at 12x peak with current routing ratios.\n- Rehearse rollback for order management, returns, and back-office services.\n- Verify capacity headroom and failover to monolith if required.\n- Certify only if all critical SLOs are met under peak stress.", "dependencies": ["S13", "S14", "S15"]}, {"step_id": "S17", "title": "Decommission monolith code and data ownership for migrated modules", "description": "Remove the old implementation only after both peaks and after all services have proven parity.\n- Remove feature flags and dark-launch paths for customer, catalogue, pricing, inventory, cart/checkout, orders, and returns.\n- Drop or archive monolith tables and stored procedures for migrated modules after data reconciliation.\n- Delete monolith code paths and reduce monolith size; verify no cross-module joins remain.\n- Run final reconciliation and zero-downtime data archival.", "dependencies": ["S16"]}, {"step_id": "S18", "title": "Final cutover, contract freeze and monolith retirement", "description": "Complete the strangler migration so the monolith stops serving business traffic.\n- Switch any remaining traffic to services and remove edge routing to the monolith.\n- Decommission monolith deployments; maintain a read-only archive for audit.\n- Finalise API contracts, versioning, and service ownership.\n- Complete security reviews and production runbooks for all services.", "dependencies": ["S17"]}, {"step_id": "S19", "title": "Continuous improvement and governance handover", "description": "Make the new architecture the default and keep it safe after migration.\n- Embed service ownership: each team owns build, deploy, and operate with SLOs.\n- Review cost, performance, and reliability; tune autoscaling and capacity for the next peak.\n- Update architecture decision records, runbooks, and onboarding material.\n- Establish a light-touch governance board for API and data contracts.", "dependencies": ["S18"]}], "estimated_complexity": "high", "success_metrics": "- 100% of critical customer journeys remain within SLOs during migration; no unplanned downtime outside planned windows.\n- Every extraction step has a rehearsed rollback path that restores monolith behaviour in under 5 minutes.\n- Peak-season capacity maintained: January and July sales complete without capacity-related errors, with peak traffic at least 12x baseline and error rate <= 0.1%.\n- By month 12, at least 8 core business capabilities are deployed as independently deployable services from separate repositories with separate data ownership.\n- Monolith code is reduced by at least 60%, and the remaining monolith no longer owns migrated data or executes migrated stored procedures.\n- Deployment frequency increases from one release every two weeks to daily per service; lead time for changes decreases from weeks to hours.\n- Test coverage on changed code reaches at least 80%; critical pricing and checkout paths have contract and parity tests.\n- Zero data loss or irreversible data corruption during migration; reconciliation discrepancies are below 0.01% of records.\n- Feature delivery velocity remains at least equal to pre-migration levels; no feature freeze is imposed.\n- No cross-service direct database joins remain for migrated capabilities; all service data access happens through APIs or events."}

[SYSTEM]
You are an expert assistant in complex project planning.
Your task is to generate a detailed and structured action plan to reach the main objective in the most professional and most detailed way, no matter how much work or steps will be needed to perform.
Use your internal reasoning processes to think deeply about the problem and create the most comprehensive plan possible.
Take as much time and space as you need to think through all aspects of the problem.
After your thorough analysis, answer with the plan in the requested structure.

Every text field you write will be read by a busy person, so write for them: short sentences, one idea each, in short paragraphs. Text fields accept Markdown: separate paragraphs with a blank line, use a bullet list when you enumerate things and plain paragraphs when you explain or argue, and bold at most one key phrase per paragraph. A text of more than three sentences must be split into paragraphs; never deliver a single unbroken block of text.

[HUMAN]
Main Objective: "A mid-size European fashion retailer runs its e-commerce on a 10-year-old monolith: Java 8 / Spring, about 2 million lines, one PostgreSQL database of 1.2 TB with 350 tables and heavy use of stored procedures and cross-module joins. It serves 8 countries, 3 currencies and 4 languages, with about 40,000 orders a day and peaks of 12x during sales. The monolith contains: the storefront (server-rendered, plus a separate mobile app hitting the same endpoints), catalogue and search (a Lucene index rebuilt nightly), pricing and promotions (the most complex module, 200,000 lines, with country-specific rules nobody fully understands), cart and checkout with three payment providers, order management, inventory synchronised every 15 minutes with the warehouse system via file exchange, customer accounts and loyalty, returns, and a back-office used by 300 staff. Deployments happen every two weeks as a single artefact with a 30-minute maintenance window; test coverage is 25% and mostly unit tests. Five teams of 8 developers, each organised around a business area but all committing to the same repository.

Plan the migration to independently deployable services over 12 months with no feature freeze, no unplanned downtime and the ability to roll back every step, preserving the peak-season capacity (sales in January and July must not be put at risk)."

Generate an initial, detailed, and well-structured plan proposal.

The plan has these parts:
- "steps": the list of steps, each with "step_id" (a short label unique within this proposal, such as S1, S2, S3), "title" (a short, descriptive title), "description" (what the step does and how, in Markdown: a short opening paragraph and then, if there are several concrete things to say, a bullet list) and "dependencies" (the step_ids of the other steps of THIS proposal that must be completed first; empty if none).
- "estimated_complexity": "low", "medium" or "high".
- "success_metrics": clear and measurable success metrics, one per line as a bullet list.

Think deeply about the problem, considering all aspects and implications. Develop a comprehensive understanding of what needs to be done to achieve the objective, then produce the plan.

{"steps": [{"step_id": "S1", "title": "Full-Scope Discovery and Dependency Mapping", "description": "Perform a **complete technical and organisational audit** of the monolith before any code changes.\n\n- Run static-analysis tools (jQAssistant, ArchUnit, custom SQL scripts) to produce a module-level dependency graph across all 2 M lines of Java and all 350 PostgreSQL tables.\n- Catalogue every stored procedure, trigger, and cross-module join; classify each as *local*, *cross-module read*, or *cross-module write*.\n- Interview each of the five teams to document tribal knowledge, especially the pricing & promotions rules (200 K lines, country-specific logic).\n- Map all external integrations: three payment providers, warehouse file exchange, mobile-app endpoints, back-office UI routes.\n- Record current performance baselines: p50 / p95 / p99 latency per endpoint, throughput, DB query plans for the top-100 queries.\n- Deliverable: a living architecture dossier stored in a shared wiki, updated throughout the migration.", "dependencies": []}, {"step_id": "S2", "title": "Build CI/CD Pipelines and Feature-Flag Platform", "description": "Create the **deployment and release-safety infrastructure** that every later step depends on.\n\n- Stand up a CI/CD stack (e.g. GitLab CI or GitHub Actions → ArgoCD) capable of building, testing, and deploying individual modules independently.\n- Introduce a feature-flag platform (LaunchDarkly, Flagsmith, or Unleash) wired into the monolith via a thin SDK; every new or changed code path ships behind a flag.\n- Define branching strategy: one repo per future service, plus the existing monorepo during the transition period.\n- Automate canary and blue-green deployment patterns so every release can be rolled back in under five minutes.\n- Target: reduce the two-week release cycle to **daily deployable** by end of this step.", "dependencies": ["S1"]}, {"step_id": "S3", "title": "Establish Observability, Tracing, and SLO Baseline", "description": "Instrument the monolith so that **every subsequent extraction is measurable** and regressions are caught within minutes.\n\n- Deploy OpenTelemetry agents across all application nodes; export traces, metrics, and structured logs to a central stack (Grafana Tempo + Prometheus + Loki, or Datadog).\n- Define SLOs per domain: storefront p99 < 400 ms, checkout p99 < 1.2 s, search p95 < 300 ms, back-office p95 < 2 s.\n- Build real-time dashboards per SLO with alerting thresholds; wire alerts to on-call rotation.\n- Implement synthetic transaction monitoring covering the critical user journeys (browse → cart → checkout → payment → confirmation) across all 8 countries, 3 currencies, and 4 languages.\n- Establish an error-budget policy: any extraction step that breaches its SLO budget is automatically rolled back.", "dependencies": ["S1"]}, {"step_id": "S4", "title": "Automated Testing Uplift and Contract-Test Foundation", "description": "Raise test coverage from **25 % to at least 60 %** on the paths that will be touched first, and introduce contract testing.\n\n- Use mutation testing (PIT) to identify the highest-risk untested paths; prioritise checkout, payment, and inventory flows.\n- Add integration tests using Testcontainers with a seeded copy of the production schema.\n- Introduce Pact (or Spring Cloud Contract) for consumer-driven contract tests between every pair of modules that will become separate services.\n- Build a regression suite of end-to-end smoke tests runnable in < 15 minutes, executed on every deploy.\n- Define a policy: no extraction proceeds unless the affected module reaches the agreed coverage threshold.", "dependencies": ["S2"]}, {"step_id": "S5", "title": "Team Topology Realignment and Governance Model", "description": "Reorganise the five teams into **stream-aligned, domain-owned squads** and agree on governance rules for the migration.\n\n- Map each team to a bounded context: (1) Storefront & Search, (2) Pricing & Promotions, (3) Cart, Checkout & Payments, (4) Order Management, Inventory & Returns, (5) Customer, Loyalty & Back-Office.\n- Assign a Platform/Enablement guild (2–3 senior engineers drawn across teams) responsible for shared infra, libraries, and cross-cutting concerns.\n- Agree on API governance: versioning policy (URL-path major, header minor), deprecation window (minimum 90 days), and an internal API catalogue.\n- Set up a weekly cross-team architecture sync and a migration-risk register reviewed every sprint.\n- Define the rollback decision tree: who can trigger a rollback, under what SLO breach, and the communication protocol.", "dependencies": ["S1"]}, {"step_id": "S6", "title": "Strangler-Fig Gateway and Anti-Corruption Layer", "description": "Deploy an **API gateway in front of the monolith** that will route traffic to either the legacy code or the new services, enabling incremental extraction.\n\n- Place a reverse-proxy / service mesh layer (e.g. Kong, Envoy via Istio, or AWS ALB + App Mesh) in front of the existing load balancer.\n- Implement an Anti-Corruption Layer (ACL) service that translates between the monolith's internal models and the new service APIs.\n- Configure the gateway to route by URL pattern, header, or feature flag; default route goes to the monolith.\n- Support traffic mirroring (shadow traffic) so new services can be validated against live production traffic before receiving real requests.\n- All mobile-app and back-office traffic passes through the gateway from day one; server-rendered pages are proxied transparently.", "dependencies": ["S2", "S3"]}, {"step_id": "S7", "title": "Database Decomposition Strategy and Shared-Data Refactor", "description": "Prepare the **1.2 TB PostgreSQL database** for eventual per-service ownership without a big-bang migration.\n\n- Classify all 350 tables by bounded context using the dependency map from S1.\n- Eliminate cross-module joins at the application layer first: replace them with service calls or denormalised read models.\n- Convert stored procedures that span contexts into application-level logic behind the ACL; keep single-context procedures temporarily.\n- Introduce an internal event log (outbox pattern) on the existing database: every state change publishes a row to an `outbox` table, later relayed to a message broker.\n- Define the target data-ownership matrix: which service will own which tables, and which data will be replicated read-only.\n- Plan a dual-write / change-data-capture (CDC) strategy using Debezium so that during transition both old and new stores stay consistent.", "dependencies": ["S1", "S4"]}, {"step_id": "S8", "title": "Event-Driven Backbone and Async Messaging Layer", "description": "Stand up the **messaging infrastructure** that decouples services and replaces synchronous cross-module calls.\n\n- Deploy Apache Kafka (or AWS MSK) with topics per bounded context: `catalogue-events`, `order-events`, `inventory-events`, `pricing-events`, `customer-events`.\n- Implement the transactional outbox relay (Debezium → Kafka Connect) so the monolith can publish domain events without code changes to business logic.\n- Define event schemas in a central Schema Registry (Avro / Protobuf) with backward-compatibility enforcement.\n- Add idempotent consumer patterns and dead-letter queues from day one.\n- Validate throughput: the backbone must sustain 12× peak (≈ 480 000 orders/day equivalent event volume) with headroom.", "dependencies": ["S6", "S7"]}, {"step_id": "S9", "title": "Containerisation and Kubernetes Platform Readiness", "description": "Package the monolith and prepare a **Kubernetes-based runtime** for all future services.\n\n- Dockerise the existing monolith (multi-stage build, slim JRE image) and deploy it to a Kubernetes cluster alongside the gateway.\n- Provision namespaces per bounded context, with network policies enforcing that only the gateway and the ACL can reach the monolith.\n- Configure horizontal pod autoscaling, pod disruption budgets, and resource quotas sized for 12× peak.\n- Set up a service mesh (Istio or Linkerd) for mTLS, traffic splitting, circuit breaking, and retry policies.\n- Run a load test replicating the January-sale profile (12× normal traffic) to validate the platform before any service extraction.", "dependencies": ["S2", "S3"]}, {"step_id": "S10", "title": "Extract Customer Accounts and Loyalty Service (Wave 1)", "description": "Carve out the **lowest-risk, well-bounded domain** first to validate the full extraction playbook.\n\n- Build a new `customer-service` (Java 21 / Spring Boot 3 or Kotlin) exposing REST + gRPC APIs for registration, authentication, profile, and loyalty points.\n- Migrate the relevant 15–20 tables to a dedicated PostgreSQL instance using the CDC dual-write pattern from S7.\n- Place the service behind the ACL; route traffic via feature flags starting at 1 % → 10 % → 50 % → 100 % over two weeks.\n- The monolith continues to serve as fallback; a single flag flip routes 100 % back.\n- Validate contract tests, SLO dashboards, and rollback procedure end-to-end.\n- This extraction serves as the **reference implementation** for all subsequent waves.", "dependencies": ["S4", "S6", "S7", "S8", "S9"]}, {"step_id": "S11", "title": "Extract Catalogue and Search Service (Wave 2)", "description": "Replace the nightly Lucene rebuild with a **real-time search and catalogue service**.\n\n- Build a `catalogue-service` owning product data, categories, and media references; use CDC from the monolith DB during transition.\n- Replace Lucene with Elasticsearch or OpenSearch; index updates driven by Kafka events instead of the nightly batch.\n- Expose search and browse APIs through the gateway; server-rendered storefront pages call the new API via the ACL.\n- Migrate in two sub-phases: (a) read-only catalogue and search behind flags, (b) write path (product updates from back-office) once reads are stable.\n- Keep the legacy Lucene index warm for instant rollback for 60 days.\n- Validate that search latency meets the p95 < 300 ms SLO across all 4 languages.", "dependencies": ["S10"]}, {"step_id": "S12", "title": "Extract Inventory and Warehouse Sync Service (Wave 3)", "description": "Isolate the **inventory domain and its 15-minute file-exchange** with the warehouse system.\n\n- Build an `inventory-service` owning stock levels, reservations, and warehouse synchronisation.\n- Replace the file-based exchange with an event-driven adapter: the service consumes warehouse updates via SFTP poll or API and publishes `inventory-updated` events to Kafka.\n- During transition, run the adapter in parallel with the legacy file job; reconcile counts nightly.\n- Checkout and order-management modules consume inventory availability via synchronous gRPC (with circuit breaker) and asynchronous events for reservation confirmations.\n- Migrate stock tables using CDC; rollback path re-points reads to the monolith tables.\n- Validate under 12× peak load: inventory checks must not become a bottleneck during flash sales.", "dependencies": ["S10"]}, {"step_id": "S13", "title": "Deep Analysis and Rule Documentation for Pricing & Promotions", "description": "Before touching the **most complex 200 K-line module**, invest in understanding and documenting its rules.\n\n- Pair domain experts from each of the 8 country teams with developers to walk through every pricing rule, promotion type, and country-specific override.\n- Produce a machine-readable rule catalogue (decision tables or a lightweight DSL) that captures all 200+ identified rules.\n- Identify dead code, redundant branches, and rules that have not fired in the last 24 months (use production logging and feature-flag data).\n- Classify rules into: (a) universal, (b) country-specific, (c) campaign/temporary.\n- Define the target architecture: a `pricing-service` with a rules engine (Drools, Easy Rules, or a custom evaluation pipeline) externalised from application code.\n- Deliverable: a signed-off rule specification document that all five teams agree represents current behaviour.", "dependencies": ["S1"]}, {"step_id": "S14", "title": "Extract Pricing and Promotions Service (Wave 4)", "description": "Rebuild the **highest-risk module** as an independent service using the documented rule set from S13.\n\n- Build a `pricing-service` with a pluggable rules engine; encode the rule catalogue from S13 as configuration rather than hard-coded Java.\n- Expose two API surfaces: synchronous price calculation (called by cart/checkout) and asynchronous promotion evaluation (event-driven for campaign changes).\n- Run the new service in **shadow mode** for 4–6 weeks: every pricing request is sent to both the monolith and the new service; a comparator flags discrepancies.\n- Only after the discrepancy rate drops below 0.01 % over two full weeks (including a weekend) begin traffic shifting via feature flags.\n- Migrate pricing tables via CDC; keep monolith pricing logic compilable and deployable as rollback for 90 days.\n- Assign dedicated on-call coverage for the first 30 days post-cutover.", "dependencies": ["S11", "S12", "S13"]}, {"step_id": "S15", "title": "Extract Cart, Checkout, and Payment Service (Wave 5)", "description": "Separate the **revenue-critical checkout flow** into its own service with hardened payment integration.\n\n- Build a `checkout-service` owning cart state, checkout orchestration, and integration with the three payment providers.\n- Cart state moves to a dedicated data store (Redis for transient cart, PostgreSQL for persisted orders) with CDC from the monolith during transition.\n- Payment-provider integrations are wrapped in an adapter layer with circuit breakers and idempotency keys; failover order between providers is configurable per country.\n- Migrate in sub-phases: (a) cart operations, (b) checkout orchestration, (c) payment capture and confirmation.\n- Run chaos-engineering tests (payment-provider timeout, partial failure) before enabling real traffic.\n- Rollback: feature flag routes checkout back to monolith; in-flight transactions are drained gracefully.", "dependencies": ["S14"]}, {"step_id": "S16", "title": "Extract Order Management and Returns Service (Wave 6)", "description": "Move **post-purchase order lifecycle and returns processing** into a dedicated service.\n\n- Build an `order-service` consuming `order-placed` events from checkout; it owns order state machine, fulfilment tracking, and returns workflow.\n- Integrate with the inventory service for reservation release and with the warehouse adapter for shipping updates.\n- Back-office order views call the new service API through the gateway; legacy views remain as fallback.\n- Migrate order and returns tables via CDC; reconcile daily during the 60-day dual-run window.\n- Validate that the returns process (including cross-border returns across the 8 countries) works identically.\n- Rollback re-routes order queries to the monolith; event replay ensures no order is lost.", "dependencies": ["S15"]}, {"step_id": "S17", "title": "Extract Back-Office and Admin Portal (Wave 7)", "description": "Deliver a **modern back-office** for the 300 staff users, consuming the new service APIs.\n\n- Build a new back-office frontend (React or Vue SPA) backed by a thin BFF (Backend-for-Frontend) that aggregates calls to catalogue, pricing, order, inventory, and customer services.\n- Migrate back-office routes incrementally via the gateway; legacy server-rendered admin pages remain accessible.\n- Implement role-based access control (RBAC) and audit logging as cross-cutting concerns in the BFF.\n- Run parallel operation for 4 weeks: staff use the new portal with a feedback channel; legacy portal stays one click away.\n- Decommission legacy admin screens only after 30 days of zero critical issues.\n- Provide training sessions and documentation for all 300 back-office users.", "dependencies": ["S16"]}, {"step_id": "S18", "title": "Storefront Modernisation and Mobile-App API Alignment", "description": "Update the **customer-facing storefront and mobile-app integration** to consume the new service layer.\n\n- Refactor the server-rendered storefront to call service APIs via the gateway instead of hitting monolith endpoints directly.\n- Introduce a Storefront BFF that aggregates catalogue, pricing, cart, and customer data for page rendering.\n- Ensure the mobile app switches to the new API version behind the gateway; enforce backward compatibility for two app-release cycles.\n- Implement edge caching (CDN + Varnish) for catalogue and search responses to protect services during 12× peaks.\n- Validate all 4 language / 3 currency combinations through automated E2E tests.\n- Rollback: gateway routes storefront traffic back to the monolith rendering path.", "dependencies": ["S11", "S14", "S15"]}, {"step_id": "S19", "title": "Peak-Season Load Testing and Resilience Validation", "description": "Prove the platform sustains **12× peak load** before the January and July sales windows.\n\n- Build a load-test suite (Gatling or k6) replicating the full user journey across all 8 countries, including promo-code-heavy scenarios.\n- Execute a full 12× load test in a staging environment that mirrors production topology, data volume (1.2 TB replica), and service versions.\n- Run chaos-engineering experiments: kill random pods, introduce network latency, take a payment provider offline, simulate Kafka broker loss.\n- Validate autoscaling: confirm that pod counts scale to handle peak within 90 seconds and scale down gracefully.\n- Produce a signed-off capacity report; any component that fails the 12× test blocks go-live.\n- Schedule this step at least 3 weeks before each peak season (early December for January sales, early June for July sales).", "dependencies": ["S9", "S15", "S16"]}, {"step_id": "S20", "title": "Monolith Decommission and Final Data Migration", "description": "Retire the legacy monolith **only after all traffic is served by the new services**.\n\n- Verify that zero production requests route to the monolith for 30 consecutive days.\n- Perform a final data reconciliation: compare monolith DB checksums against service-owned databases.\n- Archive the monolith codebase and database; retain read-only access for 12 months for audit and compliance.\n- Decommission monolith infrastructure; reclaim compute and storage resources.\n- Update all runbooks, on-call rotations, and disaster-recovery plans to reference the new service topology.\n- Conduct a cross-team retrospective documenting lessons learned, technical debt incurred, and future improvement backlog.", "dependencies": ["S16", "S17", "S18", "S19"]}], "estimated_complexity": "high", "success_metrics": "- Zero unplanned downtime throughout the 12-month migration; all maintenance performed via blue-green or canary cutover.\n- Every extraction step is reversible within 5 minutes via feature-flag rollback, validated at least once in production before full cutover.\n- Deployment frequency increases from bi-weekly to at least daily per service by month 4.\n- Peak-load capacity sustained at 12× normal traffic (≈ 480 000 orders/day) with p99 latency ≤ 800 ms for checkout and ≤ 400 ms for storefront during January and July sales.\n- Test coverage on all migrated code paths reaches ≥ 80 %; contract tests exist for every inter-service boundary.\n- Monolith codebase reduced from 2 M lines to 0 lines in production by end of month 12.\n- All 350 tables are owned by exactly one service; zero cross-service direct database joins remain.\n- The three payment providers maintain ≥ 99.95 % successful transaction rate throughout the migration.\n- Back-office availability for 300 staff ≥ 99.9 % during business hours across all 8 countries.\n- Mean time to recovery (MTTR) for any single-service incident ≤ 10 minutes.\n- No degradation in order-accuracy rate (≥ 99.99 %) or inventory reconciliation accuracy (≥ 99.9 %) at any point during the migration.\n- Customer-facing error rate (5xx) stays below 0.1 % across all 8 countries, 3 currencies, and 4 languages throughout the programme."}

[SYSTEM]
You are an expert assistant in complex project planning.
Your task is to generate a detailed and structured action plan to reach the main objective in the most professional and most detailed way, no matter how much work or steps will be needed to perform.
Use your internal reasoning processes to think deeply about the problem and create the most comprehensive plan possible.
Take as much time and space as you need to think through all aspects of the problem.
After your thorough analysis, answer with the plan in the requested structure.

Every text field you write will be read by a busy person, so write for them: short sentences, one idea each, in short paragraphs. Text fields accept Markdown: separate paragraphs with a blank line, use a bullet list when you enumerate things and plain paragraphs when you explain or argue, and bold at most one key phrase per paragraph. A text of more than three sentences must be split into paragraphs; never deliver a single unbroken block of text.

[HUMAN]
Main Objective: "A mid-size European fashion retailer runs its e-commerce on a 10-year-old monolith: Java 8 / Spring, about 2 million lines, one PostgreSQL database of 1.2 TB with 350 tables and heavy use of stored procedures and cross-module joins. It serves 8 countries, 3 currencies and 4 languages, with about 40,000 orders a day and peaks of 12x during sales. The monolith contains: the storefront (server-rendered, plus a separate mobile app hitting the same endpoints), catalogue and search (a Lucene index rebuilt nightly), pricing and promotions (the most complex module, 200,000 lines, with country-specific rules nobody fully understands), cart and checkout with three payment providers, order management, inventory synchronised every 15 minutes with the warehouse system via file exchange, customer accounts and loyalty, returns, and a back-office used by 300 staff. Deployments happen every two weeks as a single artefact with a 30-minute maintenance window; test coverage is 25% and mostly unit tests. Five teams of 8 developers, each organised around a business area but all committing to the same repository.

Plan the migration to independently deployable services over 12 months with no feature freeze, no unplanned downtime and the ability to roll back every step, preserving the peak-season capacity (sales in January and July must not be put at risk)."


For your consideration and refinement, here are proposals from the previous round:

  Previous Proposal 1 (ID: 655fdafb-56dd-4ad6-bd0f-4bb657813959, Agent: claudeHaiku4.5_initial_1, LLM: anthropic/claude-haiku-4-5):
    Estimated Complexity: high
    Success Metrics: - All major business functions (catalog, pricing, orders, payments, inventory) deployed as independently releasable services by end of month 12.
- Deployment frequency increased from bi-weekly to daily or on-demand per team, with zero coordination required between teams.
- Peak-season capacity maintained: handle 40,000 orders/day baseline and 480,000 orders/day (12x peak) with P95 page load time ≤2 seconds and payment success rate >99.9%.
- Zero unplanned downtime caused by the migration during the 12-month period; any incidents are handled with automated rollback within 5 minutes.
- Test coverage increased from 25% to ≥70% across all services, with comprehensive integration test suite running on every commit.
- Mean time to recovery (MTTR) for production incidents reduced from hours to <15 minutes due to circuit breakers and fallbacks.
- Data consistency validated: automatic nightly checks confirm service data matches source-of-truth, with any discrepancies logged and investigated within 24 hours.
- Service API latency (p95) meets SLOs: catalog ≤200ms, pricing ≤300ms, inventory ≤200ms, payment ≤1000ms, with circuit breakers preventing cascading failures.
- Each service has documented runbooks, incident response procedures, and scaling playbooks; all ops and dev teams trained and confident.
- Feature delivery velocity maintained at pre-migration levels: new feature deployment time remains unchanged despite architectural changes.
    Steps (23):
    1. Establish governance and migration steering committee
       Create a governance structure to guide the 12-month migration and ensure alignment across teams.

- Define clear decision-making authority and escalation paths.
- Establish weekly steering meetings with representatives from each of the five teams plus leadership.
- Create a shared vision for service boundaries and prioritize which modules to extract first.
- Set up RACI matrix (responsible, accountable, consulted, informed) for each major service extraction.
    2. Design service architecture and system boundaries (depends on: 1)
       Map the monolith into independently deployable services with clear boundaries and synchronization points.

- Analyze the 350 tables and identify which tables belong to each business domain (catalog, pricing, orders, inventory, etc.).
- Design the data synchronization strategy for the 1.2 TB database, including which data moves to which service.
- Plan the strangler approach for each module: what gets extracted first, what depends on what.
- Define API contracts and asynchronous messaging patterns (events vs. direct calls) between services.
    3. Deploy Kubernetes infrastructure and container registry (depends on: 2)
       Build the cloud infrastructure to run containerized services at scale with redundancy and monitoring.

- Provision a production-grade Kubernetes cluster (managed service like EKS, AKS, or on-premises).
- Set up container image registry with retention policies and security scanning.
- Configure persistent storage volumes for databases and caches.
- Implement cluster networking, RBAC, and network policies for security.
    4. Implement strangler proxy and API gateway (depends on: 3)
       Deploy a reverse proxy that routes requests between the monolith and the new services, enabling gradual traffic migration.

- Deploy API gateway (e.g., Kong, Ambassador, or cloud-native option) in front of the monolith.
- Implement request routing logic to direct traffic to services or monolith based on rules.
- Add request/response logging and transformation layer for API versioning.
- Enable instant traffic rerouting and rollback if a service fails.
    5. Build feature flagging and traffic routing system (depends on: 3)
       Implement a system to control which users hit which service, enabling safe canary deployments and A/B testing.

- Choose or build a feature flag platform (LaunchDarkly, Unleash, or open source).
- Integrate with API gateway and service code to support percentage-based traffic splitting.
- Enable per-user, per-region, and per-browser routing for testing (e.g., mobile app to new service before web).
- Create dashboards for ops teams to adjust routing in real time without redeploying.
    6. Establish comprehensive observability (logs, metrics, tracing) (depends on: 3)
       Deploy centralized logging, metrics, and tracing to track behavior across services and detect issues early.

- Set up centralized log aggregation (e.g., ELK, Splunk, or cloud-native solution).
- Deploy metrics collection (Prometheus, Datadog, or equivalent) with dashboards for each service.
- Implement distributed tracing (Jaeger, Zipkin) to track requests across service boundaries.
- Define critical alerts: error rates, latency spikes, database query performance, payment transaction failures.
    7. Design event-driven data consistency architecture (depends on: 3)
       Plan how services will stay in sync when sharing data extracted from the monolith's single database.

- Design an event bus or message queue topology (Kafka, RabbitMQ, or cloud equivalent).
- Plan Change Data Capture (CDC) from the monolith to notify services when data changes.
- Define saga patterns for multi-step distributed transactions (e.g., order creation spanning multiple services).
- Document how to handle eventual consistency, conflicts, and zombie data in each service.
    8. Build inter-service communication framework (APIs and queues) (depends on: 3)
       Establish libraries and standards for how services talk to each other synchronously and asynchronously.

- Define REST or gRPC standards (authentication, versioning, error handling) for all service-to-service calls.
- Create shared libraries for message publishing/consuming (idempotency, dead-letter handling).
- Document timeout and retry policies to prevent cascading failures.
- Provide templates and SDKs to development teams so they don't reimplement these patterns.
    9. Extract catalog and search service (depends on: 4, 5, 6, 8)
       Extract the catalog and Lucene search index into its own service, starting with a low-risk module to validate the pattern.

- Move catalog module code from monolith to a new service repository.
- Containerize the service and deploy to Kubernetes.
- Keep the existing Lucene index and nightly rebuild process initially.
- Route catalog API requests through the gateway: send 10% of traffic to new service first, validate results, increase to 100%.
    10. Create independent catalog data layer with synchronization (depends on: 9, 7)
       Extract catalog tables from the shared database and sync changes from the monolith to the new service.

- Copy catalog tables to a new PostgreSQL database managed by the catalog service.
- Implement CDC (Change Data Capture) to publish catalog changes as events when the monolith updates data.
- Build catalog service to subscribe to these events and update its own tables.
- Implement consistency checks: run hourly validation that catalog service data matches monolith source-of-truth, log discrepancies.
    11. Extract customer accounts service (depends on: 4, 5, 6, 8)
       Move customer profile, login, and loyalty data into a dedicated service that other services query.

- Extract customer and loyalty tables from monolith database.
- Build service to manage customer profile, authentication, and loyalty points.
- Implement event stream for customer changes (profile updates, loyalty point transactions).
- Route customer API calls through gateway; monolith and new service share database briefly, then switch to CDC sync.
    12. Extract returns management service (depends on: 4, 5, 6, 8)
       Create a focused returns processing service to further validate the extraction pattern and learn before tackling complex modules.

- Move returns processing logic and tables from monolith.
- Build simple service with clear inputs (return requests) and outputs (refund events).
- Connect to order data via API calls (will be extracted separately) and inventory service.
- Canary traffic, monitor error rates and latency; this is the lowest-risk extraction.
    13. Audit, document, and decompose pricing/promotions business rules (depends on: 1)
       Reverse-engineer and document the complex pricing logic to enable rebuilding it as a new service. Start early in parallel with infrastructure work.

- Form a task force: architects, the original pricing team, and business analysts.
- Read through the 200k lines of pricing code; document country-specific rules, exceptions, and dependencies (which rules call which).
- Build a comprehensive spreadsheet of pricing scenarios: free shipping rules, discount types, country-specific taxes, dynamic pricing, etc.
- Extract test cases from production data: get 1,000 real orders from each country and document how pricing rules applied.
- Identify which pricing decisions depend on cart, inventory, or customer account data.
    14. Design and implement pricing/promotions service with enhanced testing (depends on: 4, 5, 6, 8, 13)
       Rebuild the pricing logic as a new microservice with a cleaner architecture and comprehensive test coverage.

- Architect the new service with clear separation: promotion evaluation, tax calculation, discount application, price transformation per country.
- Implement each country's rules as either code or a rules engine (not hardcoded strings).
- Build unit tests for 100+ pricing scenarios (cross-reference with S13 test cases).
- Implement shadow traffic testing: send real production requests to both monolith and new service, log differences, investigate discrepancies before switching traffic.
    15. Implement event-driven pricing and cart synchronization (depends on: 14, 7, 9)
       Sync pricing changes and promotions between the pricing service and cart/checkout to keep pricing consistent in real time.

- Publish events when promotions are created/updated: promotion_created, promotion_updated, promotion_ended.
- Implement cart service subscription: when a cart is modified or promotion changes, recalculate cart total.
- Handle time-based promotions: if a promotion starts/ends during a customer's shopping, reflect immediately.
- Validate consistency: sample 1% of checkouts, compare price calculated by pricing service vs. what customer paid; alert if mismatch.
    16. Extract inventory management service (depends on: 4, 5, 6, 8, 10)
       Create a service that manages stock levels and warehouse synchronization, replacing the 15-minute batch sync with event-driven updates.

- Extract inventory tables and warehouse sync logic from monolith.
- Build inventory service that subscribes to warehouse file drops (replace file exchange with event publishing or direct API).
- Implement real-time inventory updates: when an order is placed, reserve stock immediately; when warehouse sends stock count, update available qty.
- Canary deploy and validate: monitor for stock mismatch errors (overselling); maintain monolith as source-of-truth with service as secondary initially.
    17. Extract payment gateway coordination service (depends on: 4, 5, 6, 8)
       Abstract the three payment providers into a dedicated service so checkout doesn't depend on external API details.

- Move payment provider logic (Stripe, PayPal, local provider) from monolith checkout to new service.
- Implement payment orchestration: route to correct provider based on country/currency, handle failures, retry logic.
- Build payment event stream: payment_initiated, payment_authorized, payment_captured, payment_failed, payment_refunded.
- Test thoroughly: use sandbox accounts, simulate failure scenarios (provider timeout, decline, network error); ensure consistent error messages to checkout.
- Use gateway to route: send payments for test users/regions to new service first.
    18. Implement resilience patterns across services (circuit breakers, fallbacks, retries) (depends on: 9, 10, 11, 12)
       Make services robust to failures of dependent services; services should handle failures gracefully, not crash the whole system.

- Install circuit breaker library (Resilience4j, Hystrix equivalent) in each service.
- Define circuit breaker policies per dependency: if catalog service is slow, circuit opens after 50 failures or 5 seconds slow response, fails fast.
- Implement fallback strategies: if pricing service is down, use cached pricing; if inventory is down, temporarily increase order-to-fulfillment delay.
- Set timeouts on all cross-service calls (e.g., cart→pricing must return in 500ms) with bulkhead pattern to prevent resource exhaustion.
- Test: use chaos monkey or chaos toolkit to inject failures (kill pods, add latency) and verify fallbacks work.
    19. Build comprehensive integration test suite (depends on: 14, 16, 17)
       Create automated tests that exercise real customer journeys across multiple services to catch bugs before production.

- Build test data setup: create products, customers, promos, inventory in test environment.
- Write end-to-end test scenarios: browse catalog → add to cart → apply promo → checkout with payment → order created → inventory updated → returns processing.
- Implement performance tests: simulate 40,000 orders/day baseline load, 480,000 orders (12x peak) burst load; validate response times and error rates.
- Add chaos tests: run scenarios while services fail (pod restart, network partition, database slow) to validate resilience.
- Run tests on every service commit and nightly against staging environment; alert on test failure.
    20. Create independent service deployment pipelines (depends on: 4, 18)
       Set up automated deployment so each service can be released independently without coordinating with other teams every two weeks.

- For each service: build → run tests → build container image → push to registry → deploy to staging with canary (5% traffic initially).
- Implement automated rollback: if error rate on new service exceeds threshold for 5 minutes, automatically route traffic back to old version and alert.
- Add manual approval gates for production: team lead reviews test results, approves, release happens with 0 downtime (health checks, graceful shutdown).
- Documentation: each team has runbook for deploying their service, rolling back, handling incidents.
- Target: enable each team to deploy 1-2 times per day if needed.
    21. Conduct load testing and peak-season capacity planning (depends on: 19, 20)
       Validate that the new service architecture can handle peak loads (40k baseline, 480k at 12x peak) without degradation.

- Load test in staging: ramp up traffic gradually, measure latency, error rate, and resource usage (CPU, memory, database connections).
- Identify bottlenecks: where does latency spike first? Is it database queries, service CPU, or network?
- For each service and the database: determine max capacity and burst capacity (e.g., catalog service handles 500 QPS sustained, 1000 QPS for 30s burst).
- Plan auto-scaling: set Kubernetes horizontal pod autoscaler min/max replicas, database read replicas, and caching layers based on results.
- Validate payment processing: simulate peak payment volume with all three providers; confirm no payments are lost or duplicated.
    22. Execute comprehensive pre-peak-season validation and simulation (depends on: 21)
       Run a full-dress rehearsal before January/July peak season to ensure the system is ready; critical gate before any further changes.

- Schedule 48-hour end-to-end test: run production-like load against all services with canary deployments to catch integration issues.
- Run disaster recovery drill: if one service is down, can customers still browse and checkout? If payment provider fails, can we use fallback?
- Customer journey validation: have real team members and friendly customers test: browse → add items → apply promo code → checkout on web and mobile apps.
- Team readiness: hold incident response drill, confirm runbooks are accurate, escalation paths clear, and alert thresholds are tuned.
- Performance sign-off: confirm P95 page load times, checkout flow time, order processing latency all meet SLOs for peak traffic.
- Go/no-go decision: leadership reviews results; if any critical issue, fix and re-test before peak season starts.
    23. Monitor, optimize, and prepare for ongoing evolution (depends on: 22)
       After validation, monitor the production system closely during peak season; optimize based on real behavior and plan next improvements.

- Daily monitoring during peak season: dashboard watching error rates, latency, payment success rate, customer support tickets.
- Real-time tuning: if one service is bottleneck, increase replicas or add caching; if database query is slow, add index (non-blocking).
- Post-peak analysis: compare actual peak performance to projections; document what assumptions were wrong, what worked well.
- Identify quick wins: which cross-service calls can be cached, which synchronous calls could be async, which services could be split further?
- Plan the next 6 months: which remaining monolith modules can be extracted, which services need optimization, should we change tech stack for any?

  Previous Proposal 2 (ID: 5f35320e-40bf-4797-a282-13ff94f785f1, Agent: gpt-5.6-terra_initial_2, LLM: openai/gpt-5.6-terra):
    Estimated Complexity: high
    Success Metrics: - No unplanned customer-facing downtime is attributable to migration work during the 12-month programme.
- Every production migration has a documented, rehearsed rollback that can be initiated within 15 minutes and preserves financial, order, payment, and inventory integrity.
- January and July sales periods complete with at least the pre-migration peak availability, conversion rate, payment approval rate, and order throughput.
- The hybrid platform sustains at least 12x observed normal load plus agreed headroom in full-path load and failover tests before each sales period.
- Critical journeys achieve at least 95% automated API, integration, contract, and end-to-end regression coverage by business-risk weighting, with 100% coverage of defined checkout, payment, order, stock, refund, and price-parity scenarios.
- Catalogue/search, inventory availability, customer/loyalty slices, order query/post-order slices, and selected checkout/payment façade capabilities are independently deployable with named ownership, SLOs, dashboards, runbooks, and on-call support.
- All extracted services have zero direct writes to another service's database, and all cross-service state propagation uses governed APIs or versioned events.
- For each migrated entity group, reconciliation identifies less than 0.01% unresolved record discrepancies and zero unresolved financial discrepancies at cutover completion.
- Pricing and promotion decision parity for any migrated rule slice is at least 99.99% against approved golden-master cases, with all remaining differences explicitly approved by business owners.
- Deployment frequency for independently deployable services reaches at least weekly, with no mandatory monolith maintenance window required for routine compatible releases.
- Mean time to detect critical customer-journey failures is below 5 minutes, and mean time to restore or roll back migration-related severity-one incidents is below 30 minutes.
- Feature delivery continues throughout the programme, with planned business roadmap throughput maintained at no less than 80% of the agreed baseline.
    Steps (20):
    1. Establish migration governance and delivery model
       Create a migration programme that protects revenue, peak periods, and ongoing feature delivery. Assign one accountable programme lead, a chief architect, and named business and operational owners for every domain.

- Create a steering group with engineering, product, operations, security, finance, warehouse, payments, and country representatives.
- Reserve capacity per team: 50% business delivery, 30% migration work, and 20% quality, operational, and unplanned-work reduction. Rebalance only through the steering group.
- Publish decision rights, architecture principles, risk register, dependency board, and weekly programme cadence.
- Define explicit stop/go criteria for each production cutover and a formal rollback authority.
- Plan sales protection windows: no first-time domain cutovers, database schema changes, payment changes, or major traffic experiments during the four weeks before and through January and July sales periods.
- Keep feature work flowing through the same delivery pipeline, with feature flags used to decouple code deployment from customer release.
    2. Baseline the monolith, traffic, data, and operational risk (depends on: 1)
       Build an evidence-based picture of the current system before selecting extraction order. The baseline becomes the capacity, correctness, and rollback reference for every migration wave.

- Map request flows from web, mobile, back-office, warehouse files, payment providers, and scheduled jobs to modules, tables, stored procedures, queues, and external dependencies.
- Measure normal and sale-peak throughput, latency, error rates, database load, index rebuild duration, batch duration, payment approval rates, and recovery times.
- Classify all 350 tables and stored procedures by owning business concept, writers, readers, sensitivity, retention requirements, and cross-module coupling.
- Identify critical business invariants, including stock reservation, price calculation, promotion eligibility, payment-to-order consistency, returns, loyalty accrual, and country tax requirements.
- Produce dependency heat maps and a candidate extraction scorecard using coupling, business risk, change frequency, data ownership feasibility, and expected value.
- Capture a production-like anonymised data set and documented peak-load profiles for repeatable testing.
    3. Define target architecture and domain boundaries (depends on: 2)
       Agree a pragmatic target architecture based on bounded contexts, clear data ownership, and incremental extraction. Do not start by redesigning every business process or splitting every table.

- Define initial bounded contexts: edge/storefront experience, catalogue, search, pricing and promotions, cart, checkout, payments, orders, inventory, customers and loyalty, returns, and back-office workflow.
- Assign a single system of record and an owning team for each business data entity. Services may consume replicated data but must not directly write another service's database.
- Define synchronous API rules, asynchronous event rules, versioning rules, idempotency requirements, correlation identifiers, and error-handling conventions.
- Establish a platform pattern: containerised services, managed or highly available PostgreSQL where appropriate, API gateway or edge routing, event transport, secrets management, central configuration, and infrastructure as code.
- Select an incremental strangler pattern. New services are introduced behind stable interfaces while the monolith remains the source of truth until ownership is deliberately transferred.
- Document explicitly that distributed transactions are prohibited. Use outbox, idempotent consumers, compensating actions, reconciliation, and business-visible exception queues instead.
    4. Create production safety foundations (depends on: 1, 3)
       Make every current and future component observable, operable, and auditable before material traffic is moved. This work starts in the monolith as well as in new services.

- Implement standard structured logs, metrics, distributed tracing, correlation IDs, service dashboards, synthetic customer journeys, and business KPIs.
- Define service-level objectives for storefront availability, search, price response, cart operations, checkout, payment confirmation, order creation, and warehouse export.
- Add alerting with severity, ownership, escalation paths, and tested runbooks. Alert on business failures as well as infrastructure failures.
- Establish immutable audit events for pricing changes, promotion decisions, payments, order state changes, stock adjustments, customer-data access, and administrative actions.
- Implement backup, restore, disaster recovery, and failover tests for the monolith database, new data stores, event platform, and search platform.
- Create a shared operations readiness review required before any service receives production traffic.
    5. Build secure delivery and runtime platform (depends on: 3, 4)
       Provide a paved road for independently deployable services. The platform must reduce deployment risk rather than create a second operational burden.

- Build standard service templates for Java, including health checks, readiness checks, graceful shutdown, telemetry, API documentation, authentication, configuration, database migrations, and outbox publishing.
- Implement CI/CD with build provenance, dependency and container scanning, automated unit, contract, integration, and smoke tests, environment promotion, and approval controls for high-risk releases.
- Provision isolated development, integration, staging, performance, and production environments through infrastructure as code.
- Introduce progressive delivery capabilities: feature flags, canary releases, blue/green deployment where justified, traffic splitting, automated rollback, and deployment freeze controls.
- Centralise secrets, certificate rotation, least-privilege service identities, encryption, vulnerability management, and GDPR data-handling controls.
- Ensure platform capacity is sized and load-tested for at least the documented 12x sales peak plus agreed headroom.
    6. Improve monolith safety while it remains live (depends on: 2, 4, 5)
       Stabilise the monolith so it can safely coexist with extracted services for most of the programme. The monolith remains a production dependency and needs the same operational discipline as new services.

- Add a modularity boundary map and enforce it with architecture tests, package rules, code ownership, and mandatory reviews for cross-module changes.
- Wrap high-risk database access behind repository or application interfaces, beginning with areas selected for extraction.
- Introduce expand-contract database migration rules. Additive, backward-compatible changes deploy first; destructive changes require evidence that all readers have moved.
- Raise automated regression coverage around critical journeys before touching them, using API, integration, and end-to-end tests rather than relying only on unit tests.
- Add feature flags and kill switches around all new monolith-to-service integrations.
- Reduce the 30-minute maintenance dependency by proving online deployment procedures, connection draining, backward-compatible schema releases, and zero-downtime smoke tests.
    7. Implement integration, event, and data-transition patterns (depends on: 3, 5, 6)
       Create reusable patterns for safe coexistence between the monolith and services. This is the core mechanism for reversible migration without dual-write corruption.

- Introduce an event backbone and schema registry or equivalent governance, with versioned events, retention policies, dead-letter handling, replay procedures, and consumer ownership.
- Implement transactional outbox publishing in the monolith and each service. Events are committed with source data and delivered asynchronously with deduplication.
- Provide change-data-capture only where an outbox cannot initially be added, with monitoring and a time-bound plan to replace it.
- Build a replication and reconciliation framework that compares source and target counts, hashes, business totals, lag, and exception records.
- Standardise anti-corruption adapters so services do not inherit monolith-specific data shapes and semantics.
- Define transition states for each entity: monolith-owned, replicated read model, dual-read validation, service-owned with monolith compatibility adapter, and legacy-retired.
    8. Create quality, performance, and release assurance (depends on: 2, 4, 5, 7)
       Replace confidence based on a fortnightly monolith release with automated evidence for each independently deployed component. Focus first on revenue-critical and migration-affected flows.

- Build a production-like test environment with anonymised data, external-provider simulators, warehouse-file simulators, and repeatable country, currency, language, tax, and promotion test fixtures.
- Establish consumer-driven API and event contract tests. Producers may not release breaking changes until consumers have migrated or compatibility periods expire.
- Create end-to-end tests for browse-to-order, guest and registered checkout, payment success and failure, cancellation, return, refund, stock changes, loyalty, and back-office operations.
- Implement load, soak, spike, chaos, and failover tests using the observed 12x sale profile. Run them before every traffic expansion.
- Use shadow execution for high-risk decisions. Compare service and monolith outputs without changing customer outcomes.
- Set release gates for security, contracts, performance, observability, rollback rehearsal, and business reconciliation.
    9. Select and sequence extraction waves (depends on: 2, 3, 8)
       Prioritise small, low-coupling seams first, then use the resulting capabilities for harder domains. Pricing, promotions, checkout, and core order ownership are deliberately not first-wave candidates.

- Wave 1: edge routing, read-only catalogue API, search, and selected back-office read/reporting capabilities.
- Wave 2: inventory availability read model and warehouse integration adapter, while preserving the current order and stock authority initially.
- Wave 3: customer profile and selected loyalty read/write capabilities, subject to GDPR and identity constraints.
- Wave 4: order query model, notification or non-core order workflow, and returns workflow where process boundaries are confirmed.
- Wave 5: cart and checkout façade components, followed by payment-provider adapters only after reliability evidence is sufficient.
- Treat pricing and promotions as a dedicated discovery-and-modernisation stream. Extract only verified, bounded slices after exhaustive parity testing; retain the monolith engine behind an API if full extraction is not safe within 12 months.
- Define per-wave entry criteria, exit criteria, capacity allocation, and a no-go rule for work that would cross a sales protection window.
    10. Introduce edge routing and façade interfaces (depends on: 4, 5, 6, 8)
       Decouple channels from monolith internals before extracting business capabilities. Web, mobile, and back-office clients must use stable, versioned interfaces rather than service-specific implementation details.

- Place an API gateway or backend-for-frontend layer in front of existing endpoints without changing functional behaviour.
- Route by path, tenant/country, customer cohort, feature flag, and percentage of traffic. Default routing remains to the monolith.
- Add authentication, authorisation, rate limiting, request validation, response compatibility, correlation IDs, and traffic dashboards at the edge.
- Preserve mobile API compatibility through versioning and adapter endpoints. Do not force a mobile release as a prerequisite for backend extraction.
- Implement instant route rollback to the monolith, including tested handling for sessions, carts, cached responses, and in-flight requests.
- Measure baseline response equivalence and latency overhead before moving any business endpoint.
    11. Extract catalogue read API and modern search (depends on: 7, 8, 9, 10)
       Deliver the first customer-facing extraction through read-heavy capabilities with clear fallback paths. This validates the platform, routing, replication, and operational model without changing transaction ownership.

- Build a catalogue read service fed from monolith-owned catalogue data through outbox or controlled replication.
- Replace nightly-only Lucene rebuilding with an independently operated search service that supports incremental index updates, aliases, blue/green indexes, and rapid rollback to the existing index.
- Run catalogue and search in shadow mode. Compare product availability, locale content, ranking, facets, response time, and zero-result rates against current behaviour.
- Shift traffic gradually by country and cohort. Keep the monolith catalogue/search route live until parity and peak tests pass.
- Introduce cache policies, invalidation events, stale-data limits, and cache-bypass operational controls.
- Do not make the new search service authoritative for price or stock. It displays explicitly versioned read models from their owning domains.
    12. Modernise inventory integration and availability reads (depends on: 7, 8, 9, 10)
       Separate warehouse file exchange from customer-facing inventory reads while preserving warehouse and order-system correctness. Inventory changes are operationally sensitive and require explicit freshness semantics.

- Build a warehouse integration adapter that validates, records, deduplicates, and acknowledges inbound and outbound files without changing warehouse contracts initially.
- Publish inventory-change events and create an availability read model for storefront and search use.
- Define country and fulfilment-node stock semantics, safety-stock rules, oversell tolerance, freshness targets, and customer messaging for stale or unavailable stock.
- Shadow-compare the new availability result with the monolith for all products and warehouses. Reconcile every discrepancy before traffic expansion.
- Preserve current monolith stock reservation and allocation authority until order and inventory ownership boundaries are fully designed.
- Provide an immediate fallback to monolith availability reads and a replayable file-processing recovery process.
    13. Discover and contain pricing and promotions (depends on: 2, 6, 7, 8, 9, 10)
       Treat pricing and promotions as the highest-risk business capability. First make its behaviour observable and testable; do not attempt a big-bang rewrite based on incomplete knowledge.

- Form a dedicated cross-functional squad with senior engineers, merchandising, finance, country representatives, customer support, and QA.
- Inventory all rules, stored procedures, configuration tables, overrides, scheduled jobs, manual back-office actions, and country-specific exceptions.
- Capture real production decision inputs and outputs into a privacy-safe decision log. Build a golden-master corpus covering products, customer segments, countries, currencies, dates, carts, vouchers, stacking, tax, and edge cases.
- Put the existing engine behind a versioned pricing façade. New callers use the façade even while it delegates to monolith logic.
- Build a new rules-evaluation candidate service only for well-understood rule slices. Shadow-evaluate and compare exact price, discount, explanation, and latency before any customer exposure.
- Require business sign-off, discrepancy classification, and financial-impact analysis before moving each rule slice. Keep a per-slice route-back switch to the legacy engine.
    14. Extract customer and loyalty capabilities safely (depends on: 7, 8, 9, 10)
       Move customer-facing identity-adjacent data only after privacy, consent, and data ownership are clear. Avoid introducing inconsistent account state across countries and channels.

- Define the canonical customer identifier, consent model, data-retention rules, subject-access and deletion workflows, and access-control model.
- Start with a replicated customer profile read service, then migrate bounded profile writes through a façade with idempotency and audit trails.
- Move loyalty functions in small slices, such as balance inquiry before accrual or redemption, using a ledger model and reconciliation against legacy balances.
- Support account-session compatibility across web, mobile, monolith, and new services throughout the transition.
- Reconcile customer records, consent states, and loyalty balances daily during migration. Route exceptions to trained operations staff.
- Retain a compatibility adapter for legacy back-office functions until those workflows are migrated or retired.
    15. Extract order views and bounded post-order workflows (depends on: 7, 8, 9, 10, 14)
       Create independently deployable order-related value without prematurely splitting the transactional checkout path. Start with event-driven reads and post-order processes that can tolerate asynchronous integration.

- Publish reliable order lifecycle events from the monolith using the outbox pattern.
- Build an order query service for customer-service, customer self-service, notifications, and selected back-office views. Validate it against monolith order history and live state.
- Extract bounded workflows such as notifications, selected return initiation, return-status tracking, and non-financial order enrichment where ownership is explicit.
- Preserve monolith order creation, payment capture coordination, cancellation authority, refund authority, and warehouse order export until their transition design is approved.
- Implement reconciliation for order counts, states, refunds, returns, notification delivery, and event lag.
- Ensure every new order-facing view identifies source freshness and has a monolith fallback for support staff.
    16. Create cart, checkout, and payment transition architecture (depends on: 7, 8, 9, 10, 11, 12, 13, 15)
       Prepare the revenue-critical transactional path through façade-first migration, exhaustive provider testing, and progressive traffic control. This stage must not force immediate service ownership transfer.

- Define cart identity, guest-to-account merge rules, session persistence, currency and country transitions, promotion snapshots, inventory checks, and checkout idempotency keys.
- Introduce a checkout façade that initially delegates to the monolith. Route storefront and mobile gradually while maintaining response and error compatibility.
- Isolate each payment provider behind versioned adapters with token handling, webhook verification, idempotent authorisation and capture, retry policy, reconciliation, and provider-specific fallback behaviour.
- Build a payment ledger and daily reconciliation process covering authorisations, captures, refunds, chargebacks, provider settlements, and orders.
- Shadow-run checkout orchestration and payment-adapter decisions where possible. Use provider test environments and controlled internal cohorts before customer traffic.
- Do not split the final order-creation transaction until failure-mode analysis, compensating actions, support procedures, and sale-peak load tests demonstrate acceptable risk.
    17. Transfer ownership through controlled data cutovers (depends on: 7, 8, 11, 12, 13, 14, 15, 16)
       Move write ownership one entity group at a time after services have proven read parity and operational maturity. Each cutover is a reversible state transition, not a one-time database migration.

- For each entity, document source of truth, writer cutover sequence, replication direction, API consumers, data-retention obligations, reconciliation rules, and rollback point.
- Use expand-contract schemas, backfills with checksums, change capture or outbox replication, dual-read validation, and carefully bounded write cutovers.
- Avoid unrestricted dual writes. During transition, route writes through one command owner that publishes changes reliably to dependent systems.
- Reconcile continuously by identifiers, row counts, hashes, financial totals, stock totals, and business state transitions. Define thresholds that automatically halt traffic expansion.
- Retain legacy data read access and compatibility APIs until all consumers are migrated and the defined observation period has passed.
- Schedule any high-risk ownership cutover outside sales protection windows, with an approved rollback rehearsal and staffed hypercare period.
    18. Execute progressive traffic migration and rollback drills (depends on: 4, 8, 10, 11, 12, 13, 14, 15, 16, 17)
       Move production traffic only through measured, reversible increments. Every migration uses the same operational playbook regardless of domain.

- Progress through dark launch, shadow comparison, employee cohort, low-risk country or cohort, 1%, 5%, 25%, 50%, and full traffic stages where appropriate.
- Define quantitative promotion criteria for each stage: error rate, latency, conversion, search quality, price parity, payment approval rate, order completion, inventory discrepancy, support contacts, and reconciliation lag.
- Automate route rollback and validate it with game days. Rollback must restore a known compatible route without data loss or customer-visible duplicate operations.
- Run failure injection for dependency loss, event delay, duplicate messages, provider timeout, cache failure, database failover, and warehouse-file replay.
- Maintain staffed hypercare after each material expansion, with business, support, and engineering representatives able to pause or reverse rollout.
- Freeze traffic increases before sales protection windows. Use those windows only for monitoring, capacity verification, defect fixes with approved exceptions, and rehearsed rollback readiness.
    19. Prepare peak-season resilience and capacity certification (depends on: 4, 5, 8, 11, 12, 13, 16, 18)
       Certify both the hybrid estate and fallback paths for January and July sales. A service is not production-ready if its rollback target cannot sustain the traffic it might receive.

- Forecast peak demand by country, channel, page type, checkout step, payment provider, product launch, and warehouse activity.
- Load-test the full hybrid path at at least 12x observed baseline plus agreed headroom, including gateway, caches, monolith, services, event platform, databases, search, payment adapters, and warehouse integration.
- Test traffic reversion from each service to monolith and confirm that the monolith, database, and legacy search can absorb the reverted load.
- Establish capacity reservations, autoscaling limits, database connection limits, cache warm-up procedures, provider rate-limit agreements, and operational staffing plans.
- Conduct sale-day simulations, incident command exercises, communications rehearsals, and business-continuity tests with payment providers and warehouse stakeholders.
- Obtain formal peak-readiness sign-off from engineering, operations, commerce, finance, warehouse, and customer support.
    20. Retire legacy paths and establish steady-state service governance (depends on: 17, 18, 19)
       Conclude the 12-month programme by removing only proven-obsolete paths and making service ownership sustainable. Retain legacy components where removal would create unjustified business risk.

- Decommission monolith endpoints, batch jobs, Lucene components, table access paths, and stored procedures only after consumer inventory, data archival, reconciliation, and rollback-retention periods are complete.
- Remove temporary replication and compatibility adapters in controlled releases. Update runbooks, diagrams, recovery procedures, and ownership records at the same time.
- Measure and reduce residual monolith coupling, direct database access, synchronous dependency chains, event lag, and operational toil.
- Establish quarterly architecture reviews, API and event lifecycle governance, service scorecards, dependency upgrade policies, resilience testing, and disaster-recovery exercises.
- Conduct a post-migration review against business outcomes, incident history, delivery lead time, cost, and peak performance. Prioritise any remaining pricing, checkout, order, or database decomposition work as a funded follow-on roadmap.
- Confirm that independently deployable services have named owners, on-call coverage, SLOs, operational documentation, and tested rollback or recovery procedures.

  Previous Proposal 3 (ID: b39f2a46-559f-4920-9664-540fcdb612f4, Agent: grok-4.6_initial_3, LLM: xai/grok-4.6):
    Estimated Complexity: high
    Success Metrics: - Zero unplanned downtime attributed to migration work across the 12 months.
- Every production cutover has a practised rollback that restores the previous path in minutes.
- January and July peak capacity at or above today's 12x headroom, with no extra error-budget burn on search, cart, checkout or payments.
- Feature throughput stays at or above the current two-week train; no programme-wide feature freeze.
- At least search, catalogue, identity, inventory, pricing, cart, checkout and OMS deploy independently of the monolith artefact.
- Dual-run mismatch rate for prices and stock below an agreed threshold before each traffic shift (target: 0 on money paths).
- Golden-journey pass rate 100% on critical paths before and after each cutover.
- Monolith database coupling reduced: no new cross-context joins; stored-procedure call volume on extracted domains at zero after ownership transfer.
- Mean time to revert a bad service release under 10 minutes via flags or routing.
    Steps (25):
    1. Charter, governance and non-negotiables
       Write a short **migration charter** that product, ops, finance and all five teams sign.

Feature work never stops. Only production risk is constrained.

- Name one accountable migration lead and a weekly steering forum.
- Ban big-bang rewrites, shared-database-first splits and un-reversible cutovers.
- Require a tested rollback for every production step.
- Keep the two-week monolith release train for features until a domain is fully extracted.
    2. Peak calendar and freeze protocol (depends on: 1)
       Protect **January and July** sales with hard engineering blackouts.

No extractions, schema splits or traffic switches in the six weeks before a sale or the two weeks after, unless they are already proven and idle.

- Publish the 12-month calendar in week one.
- Freeze means no new migration risk, not a feature freeze.
- Require a peak capacity rehearsal before each blackout.
- Give ops a veto on any change that could affect checkout, payments, stock or search.
    3. Baseline architecture, data and SLOs (depends on: 1)
       Measure the live system before changing it.

Build a factual map of the 2M-line monolith, the 1.2 TB database and the real traffic shape.

- Trace the top 30 user journeys and the 350 tables they touch.
- Record p50/p95/p99, error rates and 12x peak headroom per journey.
- Inventory stored procedures, cross-module joins and file exchanges.
- Tag every endpoint used by the storefront, mobile app and back-office.
    4. Delivery platform, flags and progressive delivery (depends on: 1)
       Give every team a **safe way to ship** without the 30-minute maintenance window.

New work deploys behind flags. Old work stays on the existing train until it is ready.

- Add feature flags, weighted routing and instant revert at the edge.
- Build CI that can later publish one artefact per service.
- Keep Java 8 on the monolith. Start new services on a current LTS.
- Provide preview environments that replay production-like traffic.
    5. Observability and error budgets (depends on: 3, 4)
       Instrument the monolith as if it were already many services.

You cannot extract what you cannot see.

- Add distributed tracing, RED metrics and structured logs with correlation IDs.
- Define SLOs for search, PDP, cart, checkout, payments and back-office.
- Page on error-budget burn, not on CPU.
- Dashboards must show monolith vs new service side by side for every cutover.
    6. Safety net: journeys, contracts and load (depends on: 3)
       Raise the net where extraction will cut.

Unit coverage at 25% is not enough. Protect behaviour, not lines.

- Record golden journeys for browse, price, cart, checkout, order, return and loyalty.
- Add contract tests on every mobile and storefront endpoint.
- Capture characterization tests around stored procedures before moving them.
- Automate a 12x peak load test and run it before each sale and each major cutover.
    7. Bounded contexts and extraction backlog (depends on: 3)
       Draw domain boundaries from the business, not from the package tree.

Sequence work by **risk and coupling**, not by fashion.

- Contexts: identity, catalogue, search, pricing, inventory, cart, checkout, orders, returns, loyalty, back-office.
- Extract read-mostly and already-async seams first (search, inventory files).
- Leave pricing and checkout until dual-run and reconciliation exist.
- Rank a 12-month backlog with a rollback story on every item.
    8. Team operating model without a freeze (depends on: 1, 7)
       Keep five domain teams. Stop treating the repo as a single ownership blob.

Each team ships features in the monolith **and** prepares its future service.

- Assign a service to own per team, plus a shared platform pair.
- Code owners and module walls inside the current repository first.
- A small platform group owns gateway, flags, events, CI and data tooling.
- Product still plans features; migration work is a percentage of each sprint, not a separate freeze.
    9. Modularise the monolith in place (depends on: 6, 7)
       Create seams before you create processes.

New code may not add cross-module joins or new stored-procedure coupling.

- Split packages by bounded context with compile-time walls.
- Replace in-process calls at boundaries with interfaces (branch by abstraction).
- Document and freeze the worst pricing and checkout internals; wrap them.
- Ban new features from reaching into another team's tables.
    10. Strangler facade and instant traffic rollback (depends on: 4, 5)
       Put a reverse proxy in front of every public and mobile endpoint.

Clients keep the same URLs. You choose monolith or service per route and per percentage.

- Preserve headers, sessions, cookies and the four languages.
- Shadow traffic before any live percentage.
- Rollback is a route change, not a redeploy, and must complete in minutes.
- Storefront SSR and the mobile app stay compatible until a later BFF if needed.
    11. Events, outbox and CDC backbone (depends on: 5, 9)
       Give the monolith a **reversible integration spine**.

Services must not call each other's databases. They subscribe to facts.

- Add an outbox in the same Postgres transaction as business writes.
- CDC from the monolith for tables you do not yet own.
- Standard event names for product, price, stock, customer, order and return.
- Idempotent consumers and a dead-letter process before the first extraction.
    12. Data-change playbook: dual-write, reconcile, roll back (depends on: 11)
       Treat every data move as a campaign with an abort switch.

The 1.2 TB database stays the system of record until a service proves otherwise.

- Dual-write with the monolith write winning on conflict during trial.
- Nightly and continuous reconciliation with row-level diffs.
- Never cut stored procedures until logic has an equivalent test harness.
- Rollback means stop writes to the new store and keep serving from Postgres.
    13. Extract search as the first service (depends on: 2, 8, 10, 11, 12)
       Replace the nightly Lucene rebuild with an independently deployed **search service**.

This is read-heavy, already eventually consistent, and off the payment path.

- Index from catalogue and price events, not from a nightly dump.
- Shadow queries against current Lucene until precision/recall match.
- Shift traffic 1% → 10% → 50% → 100% with instant route rollback.
- Keep the old index warm through the next sale as a cold standby.
    14. Extract catalogue read models (depends on: 13)
       Serve product, media and localisation from a catalogue service.

Writes can stay in the monolith until editors have a new path.

- Build country and language-specific read models for eight markets.
- Keep one product identity so pricing, stock and search stay aligned.
- Cut storefront and mobile read traffic via the strangler.
- Do not move merchandising tools until reads are stable.
    15. Extract identity, accounts and session (depends on: 8, 10, 12)
       Pull login, profile, addresses and session behind a dedicated service.

Mobile and web keep the same auth cookies or tokens during the switch.

- Migrate sessions without forced logouts.
- Dual-read loyalty points until that domain is extracted.
- GDPR/export and deletion flows must work in both systems.
- Rollback restores monolith auth with no password resets.
    16. Extract inventory and warehouse sync (depends on: 8, 11, 12)
       Replace the 15-minute file exchange with an inventory service that still talks to the warehouse.

The warehouse interface stays file-based until they can change. Your side becomes events.

- Service owns ATP, reservations and oversell rules.
- Adapter keeps the existing file contract so warehouse risk is zero.
- Cart and checkout read stock from the service via API or replica.
- Prove no extra oversell versus today's 15-minute lag before a sale.
    17. Pricing archaeology and dual-run harness (depends on: 6, 9)
       Do not extract the 200k-line pricing module until you can prove equivalence.

Nobody fully understands country rules. Tests must become the spec.

- Capture production price traces for all eight countries and three currencies.
- Build a harness that replays promotions, baskets and edge SKUs.
- Freeze behavioural snapshots; new promo features implement twice until cutover.
- Only then wrap pricing behind an interface inside the monolith.
    18. Extract pricing and promotions behind dual-run (depends on: 14, 17, 12)
       Run the new pricing service in **shadow** until it matches the monolith on live baskets.

Checkout keeps using monolith prices until the error budget is clean.

- Compare every quote; alert on any currency, tax or promo mismatch.
- Shift read traffic first, then write of promo usage.
- Keep the monolith engine deployable as rollback through the next two sales.
- Country-specific rules move last, one market at a time if needed.
    19. Extract cart (depends on: 15, 16, 18)
       Move the cart after identity, catalogue, stock and price reads are stable.

Cart is stateful. Lose no baskets during cutover.

- Dual-write carts; reconcile abandoned and active baskets.
- Preserve promo application using the dual-run price API.
- Session migration must survive app versions in the wild.
- Rollback reattaches baskets to the monolith cart tables.
    20. Extract checkout and payment orchestration (depends on: 19)
       Strangle checkout without touching the three payment providers in one step.

A thin orchestration service talks to existing provider integrations first.

- Keep PCI and provider contracts stable; wrap, do not rewrite.
- Idempotent order placement with an outbox to OMS.
- Canary by country and by payment method.
- Rollback is route-plus-flag; in-flight payments complete on the old path.
    21. Extract order management (depends on: 20)
       Move post-purchase order state once checkout emits reliable events.

OMS must survive 12x peaks and warehouse files.

- Order of record shifts only after reconciliation is clean for a full weekly cycle.
- Back-office screens can still read a projection while writes move.
- Returns and finance reports stay correct during dual-run.
- Keep monolith OMS as standby through one sale after cutover.
    22. Extract returns, loyalty and remaining back-office (depends on: 15, 21)
       Peel remaining domains once orders and identity are independent.

Staff of 300 must not get a big-bang UI change.

- Returns service consumes order events and drives refunds via payment facade.
- Loyalty becomes the owner of points with dual-write from checkout.
- Back-office gets BFFs or modular UIs per domain, not a new monolith.
- Train staff per screen group; keep old screens until the new ones match.
    23. Split data ownership and retire stored procedures (depends on: 16, 18, 21)
       Give each stable service its **own schema or database** only after traffic and reconciliation are boring.

Shared Postgres is allowed during transition. It is not the end state.

- Move table ownership context by context; no cross-service joins.
- Rewrite stored procedures into service code with the characterization harness.
- Shrink the 1.2 TB monolith database as tables go dark.
- Rollback is restoring replication to the monolith copy, practised in staging.
    24. Independent deploy pipelines and repository split (depends on: 8, 23)
       When a service is independently releasable, stop bundling it into the fortnightly artefact.

Teams ship on demand with automated checks and progressive delivery.

- One pipeline per service: test, canary, promote, revert.
- Split repos only after module walls and CI already work in the monorepo.
- Contract tests gate consumer and provider deploys.
- The remaining monolith keeps the old two-week train until it is small.
    25. Peak rehearsals, chaos and residual shrink (depends on: 2, 22, 24)
       Prove **12x capacity** on the mixed architecture before each sale.

Then keep shrinking the monolith so it is a shell, not a risk.

- Game-days: provider failure, CDC lag, flag revert, search fallback, stock file delay.
- Scale tests on checkout, search and inventory with production-sized data.
- Delete dead modules, jobs and tables only after two quiet weeks.
- End state: independently deployable services, instant rollback still in place, no unplanned downtime attributed to the programme.

  Previous Proposal 4 (ID: 9219a465-1887-4823-81a8-3cbfc3bb545b, Agent: deepseek-v4-pro_initial_4, LLM: deepseek/deepseek-v4-pro):
    Estimated Complexity: high
    Success Metrics: - 100% of critical customer journeys remain within SLOs during migration; no unplanned downtime outside planned windows.
- Every extraction step has a rehearsed rollback path that restores monolith behaviour in under 5 minutes.
- Peak-season capacity maintained: January and July sales complete without capacity-related errors, with peak traffic at least 12x baseline and error rate <= 0.1%.
- By month 12, at least 8 core business capabilities are deployed as independently deployable services from separate repositories with separate data ownership.
- Monolith code is reduced by at least 60%, and the remaining monolith no longer owns migrated data or executes migrated stored procedures.
- Deployment frequency increases from one release every two weeks to daily per service; lead time for changes decreases from weeks to hours.
- Test coverage on changed code reaches at least 80%; critical pricing and checkout paths have contract and parity tests.
- Zero data loss or irreversible data corruption during migration; reconciliation discrepancies are below 0.01% of records.
- Feature delivery velocity remains at least equal to pre-migration levels; no feature freeze is imposed.
- No cross-service direct database joins remain for migrated capabilities; all service data access happens through APIs or events.
    Steps (19):
    1. Baseline and decompose the monolith into bounded contexts
       Capture the current behaviour, data model, and operational risks before changing anything. The output is a shared map that justifies every later cutover.
- Inventory all modules, endpoints, database tables, stored procedures, cross-module joins, external integrations, and batch jobs.
- Map business capabilities to bounded contexts and identify candidate service seams and data owners.
- Record every country/currency/language variation, especially the 200k-line pricing and promotions module.
- Capture the peak-season calendar, current deployment windows, known failure modes, and rollback mechanisms.
- Create a risk register with blast radius and rollback criteria for each candidate extraction.
    2. Define target service architecture and migration sequence (depends on: 1)
       Agree the target state and the guardrails before building any new service.
- Publish target decomposition: storefront, catalogue/search, pricing/promotions, cart/checkout, orders, inventory, customers/loyalty, returns, back-office.
- Define synchronous APIs, asynchronous events, idempotency, retries, sagas, and eventual consistency where required.
- Define data ownership and database-per-service strategy; prohibit cross-service joins and direct access to another service's tables.
- Define API versioning, security, tenancy, and country-specific routing.
- Choose migration sequence: start with low-risk read-heavy capabilities and delay peak-sensitive cutovers until outside sales windows.
- Set the rollback requirement: every change must be behind a flag or reversible migration with rehearsed rollback.
    3. Establish observability, SLOs and production load testing (depends on: 1)
       Make the current system measurable so cutovers are based on data, not hope.
- Add structured logs, metrics, and distributed tracing to the monolith and future services.
- Define SLOs and error budgets for storefront, catalogue, cart, checkout, payments, and order management.
- Add synthetic transactions and real-user monitoring for 8 countries, 3 currencies, and 4 languages.
- Build a performance test environment that replays production-like traffic at peak 12x volume.
- Create dashboards for golden signals, slow queries, stored procedure hotspots, and cache/index health.
    4. Build zero-downtime CI/CD and database migration automation (depends on: 2)
       This is the safety rail for every later step: frequent, reversible, low-risk deployments.
- Replace the biweekly single-artifact release with a pipeline supporting per-service builds, automated tests, security scans, and deployment.
- Introduce canary and blue-green deployment with automated rollback based on SLOs and error budgets.
- Add expand/contract database migration patterns: first add new schema, dual-write or synchronise, switch reads, then remove old schema in a later release.
- Ensure every service change is independently deployable in minutes, with no planned maintenance window.
- Use infrastructure-as-code and immutable artifacts for all environments.
    5. Strengthen tests and add contract testing before cutting seams (depends on: 3, 4)
       Raise confidence in behaviour without freezing features, focusing on seams to be extracted.
- Build characterization tests around current API endpoints, stored procedures, pricing rules, and checkout flows.
- Add consumer-driven contract tests between the monolith and new services.
- Introduce mutation testing and enforce at least 80% coverage on changed code.
- Add data-migration tests, reconciliation tests, and performance regression gates to CI/CD.
- Keep a long-running dual-read and diff harness for later services.
    6. Introduce traffic routing and feature flag platform (depends on: 4, 5)
       Enable gradual migration and instant rollback without redeploying the entire monolith.
- Deploy a feature flag system and edge/API gateway that can route traffic by customer, country, currency, language, percentage, and header.
- Add dark-launch capability to send shadow traffic to new services while the monolith remains source of truth.
- Implement kill switches that revert to monolith paths in one action.
- Integrate flags with SLO dashboards and deployment rollback.
    7. Extract customer accounts and loyalty as pilot service (depends on: 2, 3, 4, 5, 6)
       Prove the extraction playbook on a well-bounded, lower-risk capability before touching the most complex modules.
- Create a customer service owning customer, address, and loyalty data; expose a REST API with the same contracts.
- Move related monolith code behind an anti-corruption layer; run dual-writes or CDC to keep data in sync.
- Use expand/contract database migration: retain monolith tables temporarily, synchronise with the service, then switch reads/writes by flag.
- Launch to a small country and a small traffic percentage; monitor SLOs and rollback if errors exceed the error budget.
- Use the pilot to refine templates, runbooks, and training for other teams.
    8. Extract catalogue and search into a dedicated service (depends on: 3, 4, 5, 6, 7)
       Move the read-heavy catalogue and search path first, as it is valuable and relatively safe if done in shadow mode.
- Build a catalogue/search service that owns product, category, and search data; maintain the Lucene index within the service or via a dedicated index.
- Synchronise catalogue data from the monolith through CDC or events; stop cross-module joins.
- Serve storefront and mobile via the new catalogue/search API; run shadow reads against the monolith and compare.
- Route reads progressively by country and language and validate search quality, latency, and conversion.
- Keep the monolith fallback and flag-based rollback until after the peak readiness gate.
    9. Extract pricing and promotions with dual-run comparison (depends on: 7, 8)
       The most complex module; migration must be based on observed behavioural equivalence.
- Build a pricing/promotions service with country-specific rules as versioned configuration or domain rules.
- Run the new service in shadow mode on all checkout/cart/catalogue calls and compare every calculation with the monolith for months before cutover.
- Treat any divergence as a defect; require 100% parity on sampled and historical promotion scenarios before routing live traffic.
- Expose a pricing API and route live reads/writes only by country and promotion type, with immediate rollback.
- Keep the monolith promotion engine available until after all peak seasons.
    10. Extract inventory service and modernise warehouse integration (depends on: 7)
       Replace the 15-minute file exchange with safer, event-driven inventory updates while keeping the old path as fallback.
- Build an inventory service owning stock levels, reservations, and warehouse sync logic.
- Integrate with the warehouse system via API or events and keep the file exchange running in parallel for dual sync.
- Expose inventory availability and reservation APIs for cart, checkout, and back-office.
- Run reconciliation between the old file batch and the new event flow for all SKUs; eliminate divergence before cutover.
- Route inventory consumers to the service progressively, maintaining the monolith fallback.
    11. Extract cart and checkout service (depends on: 7, 8, 9, 10)
       Move the highest-value transaction path only after its dependencies are available and proven.
- Build a cart/checkout service that owns cart state and checkout workflow; it calls customer, catalogue, pricing, and inventory APIs with fallbacks.
- Integrate the three payment providers through adapters; implement idempotency, retries, and reconciliation.
- Use saga or orchestration for payment, inventory reservation, and order creation.
- Route by country, currency, and traffic percentage; start with one payment provider and one country.
- Rehearse rollback to monolith checkout and validate that no cart or payment is lost.
    12. Peak readiness gate before first sales peak (depends on: 7, 8, 9, 10, 11)
       Protect the first peak by freezing risky cutovers while allowing normal feature work through flags.
- Freeze new service cutovers and irreversible data migrations for four weeks before and during the peak.
- Run production-like load tests at 12x baseline with monolith and new services in their current routing ratios.
- Rehearse rollback for every extracted service and confirm the monolith fallback handles full load.
- Pre-scale infrastructure to at least 30% above expected peak.
- Keep on-call and war-room runbooks ready; certify only if all SLOs pass in load tests.
    13. Extract order management service after first peak (depends on: 11, 12)
       Move order persistence and lifecycle after the first peak, using events from checkout and inventory.
- Build an order service owning orders and order lines; consume order-placed events from checkout and payment.
- Replace monolith order creation and status update code behind flags.
- Backfill historical orders into the service and run reconciliation.
- Route order read/write traffic progressively; maintain the monolith fallback.
- Ensure returns and customer service integration remains consistent.
    14. Extract returns service (depends on: 13)
       Move returns and refunds out of the monolith once order and inventory services are stable.
- Build a returns service owning return requests, labels, refund settlements, and status.
- Integrate with order, inventory, and payment services via APIs and events.
- Migrate business rules country-by-country with dual-run comparison.
- Keep the monolith fallback and rollback for all return journeys.
    15. Extract back-office capabilities (depends on: 13, 14)
       Decouple internal staff tools from the monolith so operations run on the new services.
- Build a new back-office UI or modular back-office services that use the same APIs as storefront services.
- Migrate catalogue, pricing, inventory, order, customer, and returns management screens incrementally.
- Route the 300 staff users per function and country; keep the old monolith back-office available during transition.
- Automate manual processes and remove direct SQL access to migrated data.
    16. Peak readiness gate before second sales peak (depends on: 13, 14, 15)
       Repeat the readiness gate for the second peak with more services in production.
- Freeze cutovers and irreversible migrations four weeks before and during the peak.
- Run full production-like load test at 12x peak with current routing ratios.
- Rehearse rollback for order management, returns, and back-office services.
- Verify capacity headroom and failover to monolith if required.
- Certify only if all critical SLOs are met under peak stress.
    17. Decommission monolith code and data ownership for migrated modules (depends on: 16)
       Remove the old implementation only after both peaks and after all services have proven parity.
- Remove feature flags and dark-launch paths for customer, catalogue, pricing, inventory, cart/checkout, orders, and returns.
- Drop or archive monolith tables and stored procedures for migrated modules after data reconciliation.
- Delete monolith code paths and reduce monolith size; verify no cross-module joins remain.
- Run final reconciliation and zero-downtime data archival.
    18. Final cutover, contract freeze and monolith retirement (depends on: 17)
       Complete the strangler migration so the monolith stops serving business traffic.
- Switch any remaining traffic to services and remove edge routing to the monolith.
- Decommission monolith deployments; maintain a read-only archive for audit.
- Finalise API contracts, versioning, and service ownership.
- Complete security reviews and production runbooks for all services.
    19. Continuous improvement and governance handover (depends on: 18)
       Make the new architecture the default and keep it safe after migration.
- Embed service ownership: each team owns build, deploy, and operate with SLOs.
- Review cost, performance, and reliability; tune autoscaling and capacity for the next peak.
- Update architecture decision records, runbooks, and onboarding material.
- Establish a light-touch governance board for API and data contracts.

  Previous Proposal 5 (ID: 733858cb-4dad-4bb6-85c9-79a3e6d968d9, Agent: qwen3.8-max_initial_5, LLM: alibaba/qwen3.8-max):
    Estimated Complexity: high
    Success Metrics: - Zero unplanned downtime throughout the 12-month migration; all maintenance performed via blue-green or canary cutover.
- Every extraction step is reversible within 5 minutes via feature-flag rollback, validated at least once in production before full cutover.
- Deployment frequency increases from bi-weekly to at least daily per service by month 4.
- Peak-load capacity sustained at 12× normal traffic (≈ 480 000 orders/day) with p99 latency ≤ 800 ms for checkout and ≤ 400 ms for storefront during January and July sales.
- Test coverage on all migrated code paths reaches ≥ 80 %; contract tests exist for every inter-service boundary.
- Monolith codebase reduced from 2 M lines to 0 lines in production by end of month 12.
- All 350 tables are owned by exactly one service; zero cross-service direct database joins remain.
- The three payment providers maintain ≥ 99.95 % successful transaction rate throughout the migration.
- Back-office availability for 300 staff ≥ 99.9 % during business hours across all 8 countries.
- Mean time to recovery (MTTR) for any single-service incident ≤ 10 minutes.
- No degradation in order-accuracy rate (≥ 99.99 %) or inventory reconciliation accuracy (≥ 99.9 %) at any point during the migration.
- Customer-facing error rate (5xx) stays below 0.1 % across all 8 countries, 3 currencies, and 4 languages throughout the programme.
    Steps (20):
    1. Full-Scope Discovery and Dependency Mapping
       Perform a **complete technical and organisational audit** of the monolith before any code changes.

- Run static-analysis tools (jQAssistant, ArchUnit, custom SQL scripts) to produce a module-level dependency graph across all 2 M lines of Java and all 350 PostgreSQL tables.
- Catalogue every stored procedure, trigger, and cross-module join; classify each as *local*, *cross-module read*, or *cross-module write*.
- Interview each of the five teams to document tribal knowledge, especially the pricing & promotions rules (200 K lines, country-specific logic).
- Map all external integrations: three payment providers, warehouse file exchange, mobile-app endpoints, back-office UI routes.
- Record current performance baselines: p50 / p95 / p99 latency per endpoint, throughput, DB query plans for the top-100 queries.
- Deliverable: a living architecture dossier stored in a shared wiki, updated throughout the migration.
    2. Build CI/CD Pipelines and Feature-Flag Platform (depends on: 1)
       Create the **deployment and release-safety infrastructure** that every later step depends on.

- Stand up a CI/CD stack (e.g. GitLab CI or GitHub Actions → ArgoCD) capable of building, testing, and deploying individual modules independently.
- Introduce a feature-flag platform (LaunchDarkly, Flagsmith, or Unleash) wired into the monolith via a thin SDK; every new or changed code path ships behind a flag.
- Define branching strategy: one repo per future service, plus the existing monorepo during the transition period.
- Automate canary and blue-green deployment patterns so every release can be rolled back in under five minutes.
- Target: reduce the two-week release cycle to **daily deployable** by end of this step.
    3. Establish Observability, Tracing, and SLO Baseline (depends on: 1)
       Instrument the monolith so that **every subsequent extraction is measurable** and regressions are caught within minutes.

- Deploy OpenTelemetry agents across all application nodes; export traces, metrics, and structured logs to a central stack (Grafana Tempo + Prometheus + Loki, or Datadog).
- Define SLOs per domain: storefront p99 < 400 ms, checkout p99 < 1.2 s, search p95 < 300 ms, back-office p95 < 2 s.
- Build real-time dashboards per SLO with alerting thresholds; wire alerts to on-call rotation.
- Implement synthetic transaction monitoring covering the critical user journeys (browse → cart → checkout → payment → confirmation) across all 8 countries, 3 currencies, and 4 languages.
- Establish an error-budget policy: any extraction step that breaches its SLO budget is automatically rolled back.
    4. Automated Testing Uplift and Contract-Test Foundation (depends on: 2)
       Raise test coverage from **25 % to at least 60 %** on the paths that will be touched first, and introduce contract testing.

- Use mutation testing (PIT) to identify the highest-risk untested paths; prioritise checkout, payment, and inventory flows.
- Add integration tests using Testcontainers with a seeded copy of the production schema.
- Introduce Pact (or Spring Cloud Contract) for consumer-driven contract tests between every pair of modules that will become separate services.
- Build a regression suite of end-to-end smoke tests runnable in < 15 minutes, executed on every deploy.
- Define a policy: no extraction proceeds unless the affected module reaches the agreed coverage threshold.
    5. Team Topology Realignment and Governance Model (depends on: 1)
       Reorganise the five teams into **stream-aligned, domain-owned squads** and agree on governance rules for the migration.

- Map each team to a bounded context: (1) Storefront & Search, (2) Pricing & Promotions, (3) Cart, Checkout & Payments, (4) Order Management, Inventory & Returns, (5) Customer, Loyalty & Back-Office.
- Assign a Platform/Enablement guild (2–3 senior engineers drawn across teams) responsible for shared infra, libraries, and cross-cutting concerns.
- Agree on API governance: versioning policy (URL-path major, header minor), deprecation window (minimum 90 days), and an internal API catalogue.
- Set up a weekly cross-team architecture sync and a migration-risk register reviewed every sprint.
- Define the rollback decision tree: who can trigger a rollback, under what SLO breach, and the communication protocol.
    6. Strangler-Fig Gateway and Anti-Corruption Layer (depends on: 2, 3)
       Deploy an **API gateway in front of the monolith** that will route traffic to either the legacy code or the new services, enabling incremental extraction.

- Place a reverse-proxy / service mesh layer (e.g. Kong, Envoy via Istio, or AWS ALB + App Mesh) in front of the existing load balancer.
- Implement an Anti-Corruption Layer (ACL) service that translates between the monolith's internal models and the new service APIs.
- Configure the gateway to route by URL pattern, header, or feature flag; default route goes to the monolith.
- Support traffic mirroring (shadow traffic) so new services can be validated against live production traffic before receiving real requests.
- All mobile-app and back-office traffic passes through the gateway from day one; server-rendered pages are proxied transparently.
    7. Database Decomposition Strategy and Shared-Data Refactor (depends on: 1, 4)
       Prepare the **1.2 TB PostgreSQL database** for eventual per-service ownership without a big-bang migration.

- Classify all 350 tables by bounded context using the dependency map from S1.
- Eliminate cross-module joins at the application layer first: replace them with service calls or denormalised read models.
- Convert stored procedures that span contexts into application-level logic behind the ACL; keep single-context procedures temporarily.
- Introduce an internal event log (outbox pattern) on the existing database: every state change publishes a row to an `outbox` table, later relayed to a message broker.
- Define the target data-ownership matrix: which service will own which tables, and which data will be replicated read-only.
- Plan a dual-write / change-data-capture (CDC) strategy using Debezium so that during transition both old and new stores stay consistent.
    8. Event-Driven Backbone and Async Messaging Layer (depends on: 6, 7)
       Stand up the **messaging infrastructure** that decouples services and replaces synchronous cross-module calls.

- Deploy Apache Kafka (or AWS MSK) with topics per bounded context: `catalogue-events`, `order-events`, `inventory-events`, `pricing-events`, `customer-events`.
- Implement the transactional outbox relay (Debezium → Kafka Connect) so the monolith can publish domain events without code changes to business logic.
- Define event schemas in a central Schema Registry (Avro / Protobuf) with backward-compatibility enforcement.
- Add idempotent consumer patterns and dead-letter queues from day one.
- Validate throughput: the backbone must sustain 12× peak (≈ 480 000 orders/day equivalent event volume) with headroom.
    9. Containerisation and Kubernetes Platform Readiness (depends on: 2, 3)
       Package the monolith and prepare a **Kubernetes-based runtime** for all future services.

- Dockerise the existing monolith (multi-stage build, slim JRE image) and deploy it to a Kubernetes cluster alongside the gateway.
- Provision namespaces per bounded context, with network policies enforcing that only the gateway and the ACL can reach the monolith.
- Configure horizontal pod autoscaling, pod disruption budgets, and resource quotas sized for 12× peak.
- Set up a service mesh (Istio or Linkerd) for mTLS, traffic splitting, circuit breaking, and retry policies.
- Run a load test replicating the January-sale profile (12× normal traffic) to validate the platform before any service extraction.
    10. Extract Customer Accounts and Loyalty Service (Wave 1) (depends on: 4, 6, 7, 8, 9)
       Carve out the **lowest-risk, well-bounded domain** first to validate the full extraction playbook.

- Build a new `customer-service` (Java 21 / Spring Boot 3 or Kotlin) exposing REST + gRPC APIs for registration, authentication, profile, and loyalty points.
- Migrate the relevant 15–20 tables to a dedicated PostgreSQL instance using the CDC dual-write pattern from S7.
- Place the service behind the ACL; route traffic via feature flags starting at 1 % → 10 % → 50 % → 100 % over two weeks.
- The monolith continues to serve as fallback; a single flag flip routes 100 % back.
- Validate contract tests, SLO dashboards, and rollback procedure end-to-end.
- This extraction serves as the **reference implementation** for all subsequent waves.
    11. Extract Catalogue and Search Service (Wave 2) (depends on: 10)
       Replace the nightly Lucene rebuild with a **real-time search and catalogue service**.

- Build a `catalogue-service` owning product data, categories, and media references; use CDC from the monolith DB during transition.
- Replace Lucene with Elasticsearch or OpenSearch; index updates driven by Kafka events instead of the nightly batch.
- Expose search and browse APIs through the gateway; server-rendered storefront pages call the new API via the ACL.
- Migrate in two sub-phases: (a) read-only catalogue and search behind flags, (b) write path (product updates from back-office) once reads are stable.
- Keep the legacy Lucene index warm for instant rollback for 60 days.
- Validate that search latency meets the p95 < 300 ms SLO across all 4 languages.
    12. Extract Inventory and Warehouse Sync Service (Wave 3) (depends on: 10)
       Isolate the **inventory domain and its 15-minute file-exchange** with the warehouse system.

- Build an `inventory-service` owning stock levels, reservations, and warehouse synchronisation.
- Replace the file-based exchange with an event-driven adapter: the service consumes warehouse updates via SFTP poll or API and publishes `inventory-updated` events to Kafka.
- During transition, run the adapter in parallel with the legacy file job; reconcile counts nightly.
- Checkout and order-management modules consume inventory availability via synchronous gRPC (with circuit breaker) and asynchronous events for reservation confirmations.
- Migrate stock tables using CDC; rollback path re-points reads to the monolith tables.
- Validate under 12× peak load: inventory checks must not become a bottleneck during flash sales.
    13. Deep Analysis and Rule Documentation for Pricing & Promotions (depends on: 1)
       Before touching the **most complex 200 K-line module**, invest in understanding and documenting its rules.

- Pair domain experts from each of the 8 country teams with developers to walk through every pricing rule, promotion type, and country-specific override.
- Produce a machine-readable rule catalogue (decision tables or a lightweight DSL) that captures all 200+ identified rules.
- Identify dead code, redundant branches, and rules that have not fired in the last 24 months (use production logging and feature-flag data).
- Classify rules into: (a) universal, (b) country-specific, (c) campaign/temporary.
- Define the target architecture: a `pricing-service` with a rules engine (Drools, Easy Rules, or a custom evaluation pipeline) externalised from application code.
- Deliverable: a signed-off rule specification document that all five teams agree represents current behaviour.
    14. Extract Pricing and Promotions Service (Wave 4) (depends on: 11, 12, 13)
       Rebuild the **highest-risk module** as an independent service using the documented rule set from S13.

- Build a `pricing-service` with a pluggable rules engine; encode the rule catalogue from S13 as configuration rather than hard-coded Java.
- Expose two API surfaces: synchronous price calculation (called by cart/checkout) and asynchronous promotion evaluation (event-driven for campaign changes).
- Run the new service in **shadow mode** for 4–6 weeks: every pricing request is sent to both the monolith and the new service; a comparator flags discrepancies.
- Only after the discrepancy rate drops below 0.01 % over two full weeks (including a weekend) begin traffic shifting via feature flags.
- Migrate pricing tables via CDC; keep monolith pricing logic compilable and deployable as rollback for 90 days.
- Assign dedicated on-call coverage for the first 30 days post-cutover.
    15. Extract Cart, Checkout, and Payment Service (Wave 5) (depends on: 14)
       Separate the **revenue-critical checkout flow** into its own service with hardened payment integration.

- Build a `checkout-service` owning cart state, checkout orchestration, and integration with the three payment providers.
- Cart state moves to a dedicated data store (Redis for transient cart, PostgreSQL for persisted orders) with CDC from the monolith during transition.
- Payment-provider integrations are wrapped in an adapter layer with circuit breakers and idempotency keys; failover order between providers is configurable per country.
- Migrate in sub-phases: (a) cart operations, (b) checkout orchestration, (c) payment capture and confirmation.
- Run chaos-engineering tests (payment-provider timeout, partial failure) before enabling real traffic.
- Rollback: feature flag routes checkout back to monolith; in-flight transactions are drained gracefully.
    16. Extract Order Management and Returns Service (Wave 6) (depends on: 15)
       Move **post-purchase order lifecycle and returns processing** into a dedicated service.

- Build an `order-service` consuming `order-placed` events from checkout; it owns order state machine, fulfilment tracking, and returns workflow.
- Integrate with the inventory service for reservation release and with the warehouse adapter for shipping updates.
- Back-office order views call the new service API through the gateway; legacy views remain as fallback.
- Migrate order and returns tables via CDC; reconcile daily during the 60-day dual-run window.
- Validate that the returns process (including cross-border returns across the 8 countries) works identically.
- Rollback re-routes order queries to the monolith; event replay ensures no order is lost.
    17. Extract Back-Office and Admin Portal (Wave 7) (depends on: 16)
       Deliver a **modern back-office** for the 300 staff users, consuming the new service APIs.

- Build a new back-office frontend (React or Vue SPA) backed by a thin BFF (Backend-for-Frontend) that aggregates calls to catalogue, pricing, order, inventory, and customer services.
- Migrate back-office routes incrementally via the gateway; legacy server-rendered admin pages remain accessible.
- Implement role-based access control (RBAC) and audit logging as cross-cutting concerns in the BFF.
- Run parallel operation for 4 weeks: staff use the new portal with a feedback channel; legacy portal stays one click away.
- Decommission legacy admin screens only after 30 days of zero critical issues.
- Provide training sessions and documentation for all 300 back-office users.
    18. Storefront Modernisation and Mobile-App API Alignment (depends on: 11, 14, 15)
       Update the **customer-facing storefront and mobile-app integration** to consume the new service layer.

- Refactor the server-rendered storefront to call service APIs via the gateway instead of hitting monolith endpoints directly.
- Introduce a Storefront BFF that aggregates catalogue, pricing, cart, and customer data for page rendering.
- Ensure the mobile app switches to the new API version behind the gateway; enforce backward compatibility for two app-release cycles.
- Implement edge caching (CDN + Varnish) for catalogue and search responses to protect services during 12× peaks.
- Validate all 4 language / 3 currency combinations through automated E2E tests.
- Rollback: gateway routes storefront traffic back to the monolith rendering path.
    19. Peak-Season Load Testing and Resilience Validation (depends on: 9, 15, 16)
       Prove the platform sustains **12× peak load** before the January and July sales windows.

- Build a load-test suite (Gatling or k6) replicating the full user journey across all 8 countries, including promo-code-heavy scenarios.
- Execute a full 12× load test in a staging environment that mirrors production topology, data volume (1.2 TB replica), and service versions.
- Run chaos-engineering experiments: kill random pods, introduce network latency, take a payment provider offline, simulate Kafka broker loss.
- Validate autoscaling: confirm that pod counts scale to handle peak within 90 seconds and scale down gracefully.
- Produce a signed-off capacity report; any component that fails the 12× test blocks go-live.
- Schedule this step at least 3 weeks before each peak season (early December for January sales, early June for July sales).
    20. Monolith Decommission and Final Data Migration (depends on: 16, 17, 18, 19)
       Retire the legacy monolith **only after all traffic is served by the new services**.

- Verify that zero production requests route to the monolith for 30 consecutive days.
- Perform a final data reconciliation: compare monolith DB checksums against service-owned databases.
- Archive the monolith codebase and database; retain read-only access for 12 months for audit and compliance.
- Decommission monolith infrastructure; reclaim compute and storage resources.
- Update all runbooks, on-call rotations, and disaster-recovery plans to reference the new service topology.
- Conduct a cross-team retrospective documenting lessons learned, technical debt incurred, and future improvement backlog.

Please, considering the previous proposals as ideas that could be considered, focus on the main objective and generate an IMPROVED proposal or a completely DIFFERENT perspective if you deem it appropriate. Only if you consider any of them is amazing and impossible to improve, answer with the same proposal.

The plan has these parts:
- "steps": the list of steps, each with "step_id" (a short label unique within this proposal, such as S1, S2, S3), "title" (a short, descriptive title), "description" (what the step does and how, in Markdown: a short opening paragraph and then, if there are several concrete things to say, a bullet list) and "dependencies" (the step_ids of the other steps of THIS proposal that must be completed first; empty if none).
- "estimated_complexity": "low", "medium" or "high".
- "success_metrics": clear and measurable success metrics, one per line as a bullet list.

Think deeply about the problem, considering all aspects and implications. Develop a comprehensive understanding of what needs to be done to achieve the objective, then produce the plan.

{"steps": [{"step_id": "S1", "title": "Migration charter, governance, and peak-season blackout protocol", "description": "Establish the decision-making structure and non-negotiable constraints that protect revenue and enable long-term delivery.", "dependencies": []}, {"step_id": "S2", "title": "Baseline the monolith: architecture, data, and operational risk", "description": "Map the entire system before making changes. Document current state to become the rollback reference for every step.", "dependencies": ["S1"]}, {"step_id": "S3", "title": "Define target bounded contexts and data ownership model", "description": "Agree which service will own which tables and business entities. Plan database decomposition strategy: which domains get their own database, which share a schema within a single PostgreSQL instance, and how CDC or replication will work.", "dependencies": ["S2"]}, {"step_id": "S4", "title": "Build CI/CD, feature flags, and progressive-delivery platform", "description": "Deploy the infrastructure that allows every team to ship independently. Feature flags decouple code deployment from customer release; canary and blue-green deployments enable rollback in minutes.", "dependencies": ["S1"]}, {"step_id": "S5", "title": "Establish observability: structured logs, metrics, tracing, and SLOs", "description": "Instrument the monolith so every extraction is measurable. Define SLOs per domain (storefront latency, checkout latency, search quality, payment success rate). Alert on error-budget burn, not CPU. Without observability, you cannot tell if an extraction succeeded.", "dependencies": ["S4"]}, {"step_id": "S6", "title": "Strengthen tests and establish contract-testing foundation", "description": "Raise coverage from 25% to at least 60% on paths that will be extracted first. Introduce characterization tests around stored procedures and pricing rules before moving them. Build consumer-driven contract tests between modules that will become services.", "dependencies": ["S2", "S5"]}, {"step_id": "S7", "title": "Stabilise and modularise the monolith in place", "description": "Create seams before you create processes. Enforce module boundaries using architecture tests and code-ownership rules. Wrap high-risk database access (especially pricing and checkout) behind application interfaces. Ban new cross-module joins. This makes the monolith safer while it is still primary.", "dependencies": ["S6"]}, {"step_id": "S8", "title": "Deploy event-driven backbone: Kafka, outbox pattern, and CDC", "description": "Stand up Kafka with topics per bounded context. Implement transactional outbox publishing in the monolith: every state change publishes an event atomically with the database write. Set up CDC (Debezium) from PostgreSQL to Kafka for tables not yet owned by services. This is the reversible integration spine that allows services to coexist with the monolith without dual-write corruption.", "dependencies": ["S3", "S4"]}, {"step_id": "S9", "title": "Deploy API gateway and traffic-routing layer with instant rollback", "description": "Place a reverse proxy (Kong, Envoy, or AWS ALB) in front of the monolith. Configure routing by path, header, feature flag, and traffic percentage. Implement traffic mirroring (shadow mode) so new services validate against live production requests before receiving real traffic. Default route always returns to monolith; rollback is a route change, not a redeploy.", "dependencies": ["S4", "S7"]}, {"step_id": "S10", "title": "Discover, document, and freeze pricing and promotions rules (parallel workstream)", "description": "Form a task force with architects, original pricing team, and business analysts. Read the 200k lines of pricing code; document country-specific rules, exceptions, and dependencies. Extract real production decision traces from logs; build a test corpus with 1,000+ real orders per country. Produce a signed-off rule specification document that represents current behaviour. This workstream runs in parallel with infrastructure build so that by month 4–5, pricing extraction can begin.", "dependencies": ["S2"]}, {"step_id": "S11", "title": "Modernise warehouse integration: adapter for existing file exchange", "description": "Build an adapter that wraps the existing 15-minute file exchange. Instead of the monolith polling files, the adapter consumes files and publishes `inventory-updated` events to Kafka. The warehouse contract stays unchanged (files), but inventory changes flow through events. This enables the inventory service to be extracted later without changing warehouse systems.", "dependencies": ["S8"]}, {"step_id": "S12", "title": "Wave 1: Extract search service (read-only, nightly-batch replacement)", "description": "Carve out the simplest, lowest-risk extraction. Replace the nightly Lucene rebuild with a real-time search service. Move search index to Elasticsearch or OpenSearch; feed it via Kafka events from catalogue changes in the monolith. Run shadow queries against both Lucene and the new service; compare results. Route 1% → 10% → 50% → 100% of storefront search traffic over two weeks.", "dependencies": ["S8", "S9", "S10"]}, {"step_id": "S13", "title": "Wave 1: Extract catalogue read service", "description": "Build a catalogue service owning product data, media, categories, and localisation. Feed data from the monolith via CDC during transition. Run shadow reads comparing product availability and locale content. Route read traffic gradually by country and language. Keep the monolith as fallback for the full testing period. This validates the extraction pattern on a second service.", "dependencies": ["S12"]}, {"step_id": "S14", "title": "Peak readiness gate 1: before January/July peak (if in window)", "description": "If a major sales peak falls during months 1–4, freeze further extractions. Run production-like load tests at 12× baseline with current routing mix. Rehearse rollback for all extracted services. Certify that the monolith fallback can absorb full traffic. Obtain formal sign-off before peak season. If no peak in this window, this is a placeholder.", "dependencies": ["S13"]}, {"step_id": "S15", "title": "Wave 2: Extract customer and identity service", "description": "Move customer profile, addresses, sessions, and login behind a dedicated service. Use CDC to sync customer tables from the monolith during transition. Implement session migration without forced logouts. Dual-read loyalty points until the loyalty module is extracted. Route authentication and profile reads via feature flags starting at 1%. Rollback returns to monolith auth with no password resets.", "dependencies": ["S13", "S14"]}, {"step_id": "S16", "title": "Wave 2: Extract inventory service with warehouse adapter", "description": "Build an inventory service owning ATP (available-to-promise), reservations, and warehouse sync. Integrate the warehouse adapter (from S11) so the service consumes inventory files or API updates and publishes events. Expose inventory availability and reservation APIs to cart and checkout. Run reconciliation between old batch and new event flow for all SKUs. Route inventory reads gradually; keep monolith fallback. The monolith remains the reservation authority until order and inventory ownership are fully designed.", "dependencies": ["S15", "S11"]}, {"step_id": "S17", "title": "Wave 2: Extract pricing and promotions service (shadow mode, months 4–8)", "description": "Build a pricing service using the rule catalogue from S10. Externalise country-specific rules as configuration, not hard-coded logic. Deploy the service in shadow mode: every pricing call is sent to both monolith and new service. A comparator logs every discrepancy. Only after discrepancy rate drops below 0.01% over two full weeks (including a weekend) begin canary traffic shifting (1% → 5% → 25% → 100%) by country. Keep monolith pricing available as rollback for 90 days post-cutover.", "dependencies": ["S10", "S13", "S16"]}, {"step_id": "S18", "title": "Peak readiness gate 2: before second major peak (July if first was January)", "description": "Freeze new extractions 6 weeks before peak. Run full load test at 12× baseline with current service routing (search, catalogue, customer, inventory at various percentages). Rehearse rollback for all services. Validate capacity headroom. Certify the platform and monolith fallback for peak load. If this peak has already passed, skip.", "dependencies": ["S17"]}, {"step_id": "S19", "title": "Wave 3: Extract cart and checkout (with payment provider integration)", "description": "Build a checkout service owning cart state and checkout orchestration. Cart state moves to a dedicated data store (Redis transient, PostgreSQL persistent) using CDC from the monolith during transition. Wrap the three payment providers in adapters with circuit breakers and idempotency keys. Implement orchestration (cart → pricing API → inventory API → payment adapter → order creation). Run extensive chaos tests (payment timeouts, provider failures, network partitions). Route by country and payment method starting at 1%. Rollback re-routes checkout to monolith; in-flight transactions complete on old path.", "dependencies": ["S18"]}, {"step_id": "S20", "title": "Wave 3: Extract order management and returns", "description": "Build an order service consuming `order-placed` events from checkout. Own order lifecycle, fulfilment tracking, and returns workflow. Migrate order and returns tables via CDC; reconcile daily during 60-day dual-run window. Back-office order views call the new service API through the gateway. Validate that returns process (including cross-border returns) works identically. Rollback re-routes order queries to monolith; event replay ensures no order is lost.", "dependencies": ["S19"]}, {"step_id": "S21", "title": "Extract back-office and modernise staff portal (300 users, 8 countries)", "description": "Build a new back-office frontend (React/Vue SPA) backed by a thin BFF (Backend-for-Frontend) that aggregates calls to catalogue, pricing, order, inventory, and customer services. Migrate back-office routes incrementally via the gateway. Run parallel operation for 4 weeks: staff use new portal with feedback channel; legacy portal stays accessible. Decommission legacy admin screens only after 30 days of zero critical issues. Provide training for all 300 back-office users.", "dependencies": ["S20"]}, {"step_id": "S22", "title": "Final peak readiness validation and chaos engineering", "description": "Run full-stack load tests at 12× peak (480,000 orders/day equivalent) including all services, gateway, databases, Kafka, search, payment provider adapters, and warehouse integration. Inject failures: kill pods, introduce latency, take providers offline, simulate Kafka broker loss. Validate autoscaling, fallback paths, and MTTR. Produce capacity report. Confirm all SLOs are met under peak stress. Obtain formal sign-off from engineering, operations, and business. This is the final gate before monolith decommission.", "dependencies": ["S21"]}, {"step_id": "S23", "title": "Retire legacy paths, decommission monolith, and establish steady-state governance", "description": "After 30 days of zero unplanned downtime with 100% traffic on services, begin decommission. Remove feature flags and dark-launch paths for all extracted modules. Verify no production requests route to monolith for 30 consecutive days. Perform final data reconciliation: compare monolith DB checksums against service databases. Archive monolith codebase and database (retain read-only for 12 months for audit). Update runbooks, on-call rotations, and disaster-recovery plans. Establish service ownership, SLOs, and quarterly architecture reviews. Conduct retrospective documenting lessons learned and future roadmap.", "dependencies": ["S22"]}], "estimated_complexity": "high", "success_metrics": "\n- **Zero unplanned downtime** attributed to migration work across all 12 months; all maintenance performed via feature flags or progressive routing.\n- **Every extraction step is reversible within 5 minutes** via flag rollback or route change, validated at least once in production before full cutover.\n- **Peak-season capacity guaranteed**: January and July sales complete with baseline performance plus 12× headroom; zero capacity-related errors; p99 checkout latency ≤ 1.2 s, p95 storefront latency ≤ 400 ms.\n- **By end of month 12**: at least 8 core services independently deployable (search, catalogue, customer, inventory, pricing, cart, checkout, orders) from separate repositories with independent data ownership and daily deployment cadence.\n- **Database decomposition complete**: All 350 tables owned by exactly one service; zero cross-service direct database joins remain; shared Postgres deprecated in favour of per-service or per-bounded-context schemas.\n- **Monolith code reduced** from 2 million lines to <100k lines (legacy orchestration and audit-only components); all migrated code lives in services.\n- **Test coverage on migrated code paths** reaches ≥ 80%; contract tests exist for every inter-service API boundary and event stream.\n- **Deployment velocity transformed**: Frequency increases from bi-weekly to daily per service; lead time for changes decreases from weeks to hours.\n- **Pricing and promotions parity** maintained at ≥ 99.99% against approved golden-master cases; shadow-run discrepancies logged and resolved before traffic cutover.\n- **Payment processing resilience**: All three providers maintain ≥ 99.95% successful transaction rate throughout migration; zero payment loss or duplication.\n- **Data consistency and reconciliation**: Automatic nightly checks confirm service data matches source-of-truth; unresolved discrepancies < 0.01% of records; zero unresolved financial discrepancies.\n- **Feature delivery continues uninterrupted**: Business roadmap throughput maintained at ≥ 80% of baseline; feature work and migration work coexist in same delivery pipeline via feature flags.\n- **Back-office continuity**: 300 staff experience zero disruption during migration; new portal deployed in parallel with legacy; training delivered per user cohort.\n- **Mean time to recover (MTTR)** for any service incident ≤ 10 minutes via circuit breakers, fallbacks, and practised runbooks.\n- **Warehouse integration modernised**: Event-driven inventory updates coexist with file-based exchange; 15-minute batch sync is eliminated without warehouse-system changes."}

[SYSTEM]
You are an expert assistant in complex project planning.
Your task is to generate a detailed and structured action plan to reach the main objective in the most professional and most detailed way, no matter how much work or steps will be needed to perform.
Use your internal reasoning processes to think deeply about the problem and create the most comprehensive plan possible.
Take as much time and space as you need to think through all aspects of the problem.
After your thorough analysis, answer with the plan in the requested structure.

Every text field you write will be read by a busy person, so write for them: short sentences, one idea each, in short paragraphs. Text fields accept Markdown: separate paragraphs with a blank line, use a bullet list when you enumerate things and plain paragraphs when you explain or argue, and bold at most one key phrase per paragraph. A text of more than three sentences must be split into paragraphs; never deliver a single unbroken block of text.

[HUMAN]
Main Objective: "A mid-size European fashion retailer runs its e-commerce on a 10-year-old monolith: Java 8 / Spring, about 2 million lines, one PostgreSQL database of 1.2 TB with 350 tables and heavy use of stored procedures and cross-module joins. It serves 8 countries, 3 currencies and 4 languages, with about 40,000 orders a day and peaks of 12x during sales. The monolith contains: the storefront (server-rendered, plus a separate mobile app hitting the same endpoints), catalogue and search (a Lucene index rebuilt nightly), pricing and promotions (the most complex module, 200,000 lines, with country-specific rules nobody fully understands), cart and checkout with three payment providers, order management, inventory synchronised every 15 minutes with the warehouse system via file exchange, customer accounts and loyalty, returns, and a back-office used by 300 staff. Deployments happen every two weeks as a single artefact with a 30-minute maintenance window; test coverage is 25% and mostly unit tests. Five teams of 8 developers, each organised around a business area but all committing to the same repository.

Plan the migration to independently deployable services over 12 months with no feature freeze, no unplanned downtime and the ability to roll back every step, preserving the peak-season capacity (sales in January and July must not be put at risk)."


For your consideration and refinement, here are proposals from the previous round:

  Previous Proposal 1 (ID: 655fdafb-56dd-4ad6-bd0f-4bb657813959, Agent: claudeHaiku4.5_initial_1, LLM: anthropic/claude-haiku-4-5):
    Estimated Complexity: high
    Success Metrics: - All major business functions (catalog, pricing, orders, payments, inventory) deployed as independently releasable services by end of month 12.
- Deployment frequency increased from bi-weekly to daily or on-demand per team, with zero coordination required between teams.
- Peak-season capacity maintained: handle 40,000 orders/day baseline and 480,000 orders/day (12x peak) with P95 page load time ≤2 seconds and payment success rate >99.9%.
- Zero unplanned downtime caused by the migration during the 12-month period; any incidents are handled with automated rollback within 5 minutes.
- Test coverage increased from 25% to ≥70% across all services, with comprehensive integration test suite running on every commit.
- Mean time to recovery (MTTR) for production incidents reduced from hours to <15 minutes due to circuit breakers and fallbacks.
- Data consistency validated: automatic nightly checks confirm service data matches source-of-truth, with any discrepancies logged and investigated within 24 hours.
- Service API latency (p95) meets SLOs: catalog ≤200ms, pricing ≤300ms, inventory ≤200ms, payment ≤1000ms, with circuit breakers preventing cascading failures.
- Each service has documented runbooks, incident response procedures, and scaling playbooks; all ops and dev teams trained and confident.
- Feature delivery velocity maintained at pre-migration levels: new feature deployment time remains unchanged despite architectural changes.
    Steps (23):
    1. Establish governance and migration steering committee
       Create a governance structure to guide the 12-month migration and ensure alignment across teams.

- Define clear decision-making authority and escalation paths.
- Establish weekly steering meetings with representatives from each of the five teams plus leadership.
- Create a shared vision for service boundaries and prioritize which modules to extract first.
- Set up RACI matrix (responsible, accountable, consulted, informed) for each major service extraction.
    2. Design service architecture and system boundaries (depends on: 1)
       Map the monolith into independently deployable services with clear boundaries and synchronization points.

- Analyze the 350 tables and identify which tables belong to each business domain (catalog, pricing, orders, inventory, etc.).
- Design the data synchronization strategy for the 1.2 TB database, including which data moves to which service.
- Plan the strangler approach for each module: what gets extracted first, what depends on what.
- Define API contracts and asynchronous messaging patterns (events vs. direct calls) between services.
    3. Deploy Kubernetes infrastructure and container registry (depends on: 2)
       Build the cloud infrastructure to run containerized services at scale with redundancy and monitoring.

- Provision a production-grade Kubernetes cluster (managed service like EKS, AKS, or on-premises).
- Set up container image registry with retention policies and security scanning.
- Configure persistent storage volumes for databases and caches.
- Implement cluster networking, RBAC, and network policies for security.
    4. Implement strangler proxy and API gateway (depends on: 3)
       Deploy a reverse proxy that routes requests between the monolith and the new services, enabling gradual traffic migration.

- Deploy API gateway (e.g., Kong, Ambassador, or cloud-native option) in front of the monolith.
- Implement request routing logic to direct traffic to services or monolith based on rules.
- Add request/response logging and transformation layer for API versioning.
- Enable instant traffic rerouting and rollback if a service fails.
    5. Build feature flagging and traffic routing system (depends on: 3)
       Implement a system to control which users hit which service, enabling safe canary deployments and A/B testing.

- Choose or build a feature flag platform (LaunchDarkly, Unleash, or open source).
- Integrate with API gateway and service code to support percentage-based traffic splitting.
- Enable per-user, per-region, and per-browser routing for testing (e.g., mobile app to new service before web).
- Create dashboards for ops teams to adjust routing in real time without redeploying.
    6. Establish comprehensive observability (logs, metrics, tracing) (depends on: 3)
       Deploy centralized logging, metrics, and tracing to track behavior across services and detect issues early.

- Set up centralized log aggregation (e.g., ELK, Splunk, or cloud-native solution).
- Deploy metrics collection (Prometheus, Datadog, or equivalent) with dashboards for each service.
- Implement distributed tracing (Jaeger, Zipkin) to track requests across service boundaries.
- Define critical alerts: error rates, latency spikes, database query performance, payment transaction failures.
    7. Design event-driven data consistency architecture (depends on: 3)
       Plan how services will stay in sync when sharing data extracted from the monolith's single database.

- Design an event bus or message queue topology (Kafka, RabbitMQ, or cloud equivalent).
- Plan Change Data Capture (CDC) from the monolith to notify services when data changes.
- Define saga patterns for multi-step distributed transactions (e.g., order creation spanning multiple services).
- Document how to handle eventual consistency, conflicts, and zombie data in each service.
    8. Build inter-service communication framework (APIs and queues) (depends on: 3)
       Establish libraries and standards for how services talk to each other synchronously and asynchronously.

- Define REST or gRPC standards (authentication, versioning, error handling) for all service-to-service calls.
- Create shared libraries for message publishing/consuming (idempotency, dead-letter handling).
- Document timeout and retry policies to prevent cascading failures.
- Provide templates and SDKs to development teams so they don't reimplement these patterns.
    9. Extract catalog and search service (depends on: 4, 5, 6, 8)
       Extract the catalog and Lucene search index into its own service, starting with a low-risk module to validate the pattern.

- Move catalog module code from monolith to a new service repository.
- Containerize the service and deploy to Kubernetes.
- Keep the existing Lucene index and nightly rebuild process initially.
- Route catalog API requests through the gateway: send 10% of traffic to new service first, validate results, increase to 100%.
    10. Create independent catalog data layer with synchronization (depends on: 9, 7)
       Extract catalog tables from the shared database and sync changes from the monolith to the new service.

- Copy catalog tables to a new PostgreSQL database managed by the catalog service.
- Implement CDC (Change Data Capture) to publish catalog changes as events when the monolith updates data.
- Build catalog service to subscribe to these events and update its own tables.
- Implement consistency checks: run hourly validation that catalog service data matches monolith source-of-truth, log discrepancies.
    11. Extract customer accounts service (depends on: 4, 5, 6, 8)
       Move customer profile, login, and loyalty data into a dedicated service that other services query.

- Extract customer and loyalty tables from monolith database.
- Build service to manage customer profile, authentication, and loyalty points.
- Implement event stream for customer changes (profile updates, loyalty point transactions).
- Route customer API calls through gateway; monolith and new service share database briefly, then switch to CDC sync.
    12. Extract returns management service (depends on: 4, 5, 6, 8)
       Create a focused returns processing service to further validate the extraction pattern and learn before tackling complex modules.

- Move returns processing logic and tables from monolith.
- Build simple service with clear inputs (return requests) and outputs (refund events).
- Connect to order data via API calls (will be extracted separately) and inventory service.
- Canary traffic, monitor error rates and latency; this is the lowest-risk extraction.
    13. Audit, document, and decompose pricing/promotions business rules (depends on: 1)
       Reverse-engineer and document the complex pricing logic to enable rebuilding it as a new service. Start early in parallel with infrastructure work.

- Form a task force: architects, the original pricing team, and business analysts.
- Read through the 200k lines of pricing code; document country-specific rules, exceptions, and dependencies (which rules call which).
- Build a comprehensive spreadsheet of pricing scenarios: free shipping rules, discount types, country-specific taxes, dynamic pricing, etc.
- Extract test cases from production data: get 1,000 real orders from each country and document how pricing rules applied.
- Identify which pricing decisions depend on cart, inventory, or customer account data.
    14. Design and implement pricing/promotions service with enhanced testing (depends on: 4, 5, 6, 8, 13)
       Rebuild the pricing logic as a new microservice with a cleaner architecture and comprehensive test coverage.

- Architect the new service with clear separation: promotion evaluation, tax calculation, discount application, price transformation per country.
- Implement each country's rules as either code or a rules engine (not hardcoded strings).
- Build unit tests for 100+ pricing scenarios (cross-reference with S13 test cases).
- Implement shadow traffic testing: send real production requests to both monolith and new service, log differences, investigate discrepancies before switching traffic.
    15. Implement event-driven pricing and cart synchronization (depends on: 14, 7, 9)
       Sync pricing changes and promotions between the pricing service and cart/checkout to keep pricing consistent in real time.

- Publish events when promotions are created/updated: promotion_created, promotion_updated, promotion_ended.
- Implement cart service subscription: when a cart is modified or promotion changes, recalculate cart total.
- Handle time-based promotions: if a promotion starts/ends during a customer's shopping, reflect immediately.
- Validate consistency: sample 1% of checkouts, compare price calculated by pricing service vs. what customer paid; alert if mismatch.
    16. Extract inventory management service (depends on: 4, 5, 6, 8, 10)
       Create a service that manages stock levels and warehouse synchronization, replacing the 15-minute batch sync with event-driven updates.

- Extract inventory tables and warehouse sync logic from monolith.
- Build inventory service that subscribes to warehouse file drops (replace file exchange with event publishing or direct API).
- Implement real-time inventory updates: when an order is placed, reserve stock immediately; when warehouse sends stock count, update available qty.
- Canary deploy and validate: monitor for stock mismatch errors (overselling); maintain monolith as source-of-truth with service as secondary initially.
    17. Extract payment gateway coordination service (depends on: 4, 5, 6, 8)
       Abstract the three payment providers into a dedicated service so checkout doesn't depend on external API details.

- Move payment provider logic (Stripe, PayPal, local provider) from monolith checkout to new service.
- Implement payment orchestration: route to correct provider based on country/currency, handle failures, retry logic.
- Build payment event stream: payment_initiated, payment_authorized, payment_captured, payment_failed, payment_refunded.
- Test thoroughly: use sandbox accounts, simulate failure scenarios (provider timeout, decline, network error); ensure consistent error messages to checkout.
- Use gateway to route: send payments for test users/regions to new service first.
    18. Implement resilience patterns across services (circuit breakers, fallbacks, retries) (depends on: 9, 10, 11, 12)
       Make services robust to failures of dependent services; services should handle failures gracefully, not crash the whole system.

- Install circuit breaker library (Resilience4j, Hystrix equivalent) in each service.
- Define circuit breaker policies per dependency: if catalog service is slow, circuit opens after 50 failures or 5 seconds slow response, fails fast.
- Implement fallback strategies: if pricing service is down, use cached pricing; if inventory is down, temporarily increase order-to-fulfillment delay.
- Set timeouts on all cross-service calls (e.g., cart→pricing must return in 500ms) with bulkhead pattern to prevent resource exhaustion.
- Test: use chaos monkey or chaos toolkit to inject failures (kill pods, add latency) and verify fallbacks work.
    19. Build comprehensive integration test suite (depends on: 14, 16, 17)
       Create automated tests that exercise real customer journeys across multiple services to catch bugs before production.

- Build test data setup: create products, customers, promos, inventory in test environment.
- Write end-to-end test scenarios: browse catalog → add to cart → apply promo → checkout with payment → order created → inventory updated → returns processing.
- Implement performance tests: simulate 40,000 orders/day baseline load, 480,000 orders (12x peak) burst load; validate response times and error rates.
- Add chaos tests: run scenarios while services fail (pod restart, network partition, database slow) to validate resilience.
- Run tests on every service commit and nightly against staging environment; alert on test failure.
    20. Create independent service deployment pipelines (depends on: 4, 18)
       Set up automated deployment so each service can be released independently without coordinating with other teams every two weeks.

- For each service: build → run tests → build container image → push to registry → deploy to staging with canary (5% traffic initially).
- Implement automated rollback: if error rate on new service exceeds threshold for 5 minutes, automatically route traffic back to old version and alert.
- Add manual approval gates for production: team lead reviews test results, approves, release happens with 0 downtime (health checks, graceful shutdown).
- Documentation: each team has runbook for deploying their service, rolling back, handling incidents.
- Target: enable each team to deploy 1-2 times per day if needed.
    21. Conduct load testing and peak-season capacity planning (depends on: 19, 20)
       Validate that the new service architecture can handle peak loads (40k baseline, 480k at 12x peak) without degradation.

- Load test in staging: ramp up traffic gradually, measure latency, error rate, and resource usage (CPU, memory, database connections).
- Identify bottlenecks: where does latency spike first? Is it database queries, service CPU, or network?
- For each service and the database: determine max capacity and burst capacity (e.g., catalog service handles 500 QPS sustained, 1000 QPS for 30s burst).
- Plan auto-scaling: set Kubernetes horizontal pod autoscaler min/max replicas, database read replicas, and caching layers based on results.
- Validate payment processing: simulate peak payment volume with all three providers; confirm no payments are lost or duplicated.
    22. Execute comprehensive pre-peak-season validation and simulation (depends on: 21)
       Run a full-dress rehearsal before January/July peak season to ensure the system is ready; critical gate before any further changes.

- Schedule 48-hour end-to-end test: run production-like load against all services with canary deployments to catch integration issues.
- Run disaster recovery drill: if one service is down, can customers still browse and checkout? If payment provider fails, can we use fallback?
- Customer journey validation: have real team members and friendly customers test: browse → add items → apply promo code → checkout on web and mobile apps.
- Team readiness: hold incident response drill, confirm runbooks are accurate, escalation paths clear, and alert thresholds are tuned.
- Performance sign-off: confirm P95 page load times, checkout flow time, order processing latency all meet SLOs for peak traffic.
- Go/no-go decision: leadership reviews results; if any critical issue, fix and re-test before peak season starts.
    23. Monitor, optimize, and prepare for ongoing evolution (depends on: 22)
       After validation, monitor the production system closely during peak season; optimize based on real behavior and plan next improvements.

- Daily monitoring during peak season: dashboard watching error rates, latency, payment success rate, customer support tickets.
- Real-time tuning: if one service is bottleneck, increase replicas or add caching; if database query is slow, add index (non-blocking).
- Post-peak analysis: compare actual peak performance to projections; document what assumptions were wrong, what worked well.
- Identify quick wins: which cross-service calls can be cached, which synchronous calls could be async, which services could be split further?
- Plan the next 6 months: which remaining monolith modules can be extracted, which services need optimization, should we change tech stack for any?

  Previous Proposal 2 (ID: 5f35320e-40bf-4797-a282-13ff94f785f1, Agent: gpt-5.6-terra_initial_2, LLM: openai/gpt-5.6-terra):
    Estimated Complexity: high
    Success Metrics: - No unplanned customer-facing downtime is attributable to migration work during the 12-month programme.
- Every production migration has a documented, rehearsed rollback that can be initiated within 15 minutes and preserves financial, order, payment, and inventory integrity.
- January and July sales periods complete with at least the pre-migration peak availability, conversion rate, payment approval rate, and order throughput.
- The hybrid platform sustains at least 12x observed normal load plus agreed headroom in full-path load and failover tests before each sales period.
- Critical journeys achieve at least 95% automated API, integration, contract, and end-to-end regression coverage by business-risk weighting, with 100% coverage of defined checkout, payment, order, stock, refund, and price-parity scenarios.
- Catalogue/search, inventory availability, customer/loyalty slices, order query/post-order slices, and selected checkout/payment façade capabilities are independently deployable with named ownership, SLOs, dashboards, runbooks, and on-call support.
- All extracted services have zero direct writes to another service's database, and all cross-service state propagation uses governed APIs or versioned events.
- For each migrated entity group, reconciliation identifies less than 0.01% unresolved record discrepancies and zero unresolved financial discrepancies at cutover completion.
- Pricing and promotion decision parity for any migrated rule slice is at least 99.99% against approved golden-master cases, with all remaining differences explicitly approved by business owners.
- Deployment frequency for independently deployable services reaches at least weekly, with no mandatory monolith maintenance window required for routine compatible releases.
- Mean time to detect critical customer-journey failures is below 5 minutes, and mean time to restore or roll back migration-related severity-one incidents is below 30 minutes.
- Feature delivery continues throughout the programme, with planned business roadmap throughput maintained at no less than 80% of the agreed baseline.
    Steps (20):
    1. Establish migration governance and delivery model
       Create a migration programme that protects revenue, peak periods, and ongoing feature delivery. Assign one accountable programme lead, a chief architect, and named business and operational owners for every domain.

- Create a steering group with engineering, product, operations, security, finance, warehouse, payments, and country representatives.
- Reserve capacity per team: 50% business delivery, 30% migration work, and 20% quality, operational, and unplanned-work reduction. Rebalance only through the steering group.
- Publish decision rights, architecture principles, risk register, dependency board, and weekly programme cadence.
- Define explicit stop/go criteria for each production cutover and a formal rollback authority.
- Plan sales protection windows: no first-time domain cutovers, database schema changes, payment changes, or major traffic experiments during the four weeks before and through January and July sales periods.
- Keep feature work flowing through the same delivery pipeline, with feature flags used to decouple code deployment from customer release.
    2. Baseline the monolith, traffic, data, and operational risk (depends on: 1)
       Build an evidence-based picture of the current system before selecting extraction order. The baseline becomes the capacity, correctness, and rollback reference for every migration wave.

- Map request flows from web, mobile, back-office, warehouse files, payment providers, and scheduled jobs to modules, tables, stored procedures, queues, and external dependencies.
- Measure normal and sale-peak throughput, latency, error rates, database load, index rebuild duration, batch duration, payment approval rates, and recovery times.
- Classify all 350 tables and stored procedures by owning business concept, writers, readers, sensitivity, retention requirements, and cross-module coupling.
- Identify critical business invariants, including stock reservation, price calculation, promotion eligibility, payment-to-order consistency, returns, loyalty accrual, and country tax requirements.
- Produce dependency heat maps and a candidate extraction scorecard using coupling, business risk, change frequency, data ownership feasibility, and expected value.
- Capture a production-like anonymised data set and documented peak-load profiles for repeatable testing.
    3. Define target architecture and domain boundaries (depends on: 2)
       Agree a pragmatic target architecture based on bounded contexts, clear data ownership, and incremental extraction. Do not start by redesigning every business process or splitting every table.

- Define initial bounded contexts: edge/storefront experience, catalogue, search, pricing and promotions, cart, checkout, payments, orders, inventory, customers and loyalty, returns, and back-office workflow.
- Assign a single system of record and an owning team for each business data entity. Services may consume replicated data but must not directly write another service's database.
- Define synchronous API rules, asynchronous event rules, versioning rules, idempotency requirements, correlation identifiers, and error-handling conventions.
- Establish a platform pattern: containerised services, managed or highly available PostgreSQL where appropriate, API gateway or edge routing, event transport, secrets management, central configuration, and infrastructure as code.
- Select an incremental strangler pattern. New services are introduced behind stable interfaces while the monolith remains the source of truth until ownership is deliberately transferred.
- Document explicitly that distributed transactions are prohibited. Use outbox, idempotent consumers, compensating actions, reconciliation, and business-visible exception queues instead.
    4. Create production safety foundations (depends on: 1, 3)
       Make every current and future component observable, operable, and auditable before material traffic is moved. This work starts in the monolith as well as in new services.

- Implement standard structured logs, metrics, distributed tracing, correlation IDs, service dashboards, synthetic customer journeys, and business KPIs.
- Define service-level objectives for storefront availability, search, price response, cart operations, checkout, payment confirmation, order creation, and warehouse export.
- Add alerting with severity, ownership, escalation paths, and tested runbooks. Alert on business failures as well as infrastructure failures.
- Establish immutable audit events for pricing changes, promotion decisions, payments, order state changes, stock adjustments, customer-data access, and administrative actions.
- Implement backup, restore, disaster recovery, and failover tests for the monolith database, new data stores, event platform, and search platform.
- Create a shared operations readiness review required before any service receives production traffic.
    5. Build secure delivery and runtime platform (depends on: 3, 4)
       Provide a paved road for independently deployable services. The platform must reduce deployment risk rather than create a second operational burden.

- Build standard service templates for Java, including health checks, readiness checks, graceful shutdown, telemetry, API documentation, authentication, configuration, database migrations, and outbox publishing.
- Implement CI/CD with build provenance, dependency and container scanning, automated unit, contract, integration, and smoke tests, environment promotion, and approval controls for high-risk releases.
- Provision isolated development, integration, staging, performance, and production environments through infrastructure as code.
- Introduce progressive delivery capabilities: feature flags, canary releases, blue/green deployment where justified, traffic splitting, automated rollback, and deployment freeze controls.
- Centralise secrets, certificate rotation, least-privilege service identities, encryption, vulnerability management, and GDPR data-handling controls.
- Ensure platform capacity is sized and load-tested for at least the documented 12x sales peak plus agreed headroom.
    6. Improve monolith safety while it remains live (depends on: 2, 4, 5)
       Stabilise the monolith so it can safely coexist with extracted services for most of the programme. The monolith remains a production dependency and needs the same operational discipline as new services.

- Add a modularity boundary map and enforce it with architecture tests, package rules, code ownership, and mandatory reviews for cross-module changes.
- Wrap high-risk database access behind repository or application interfaces, beginning with areas selected for extraction.
- Introduce expand-contract database migration rules. Additive, backward-compatible changes deploy first; destructive changes require evidence that all readers have moved.
- Raise automated regression coverage around critical journeys before touching them, using API, integration, and end-to-end tests rather than relying only on unit tests.
- Add feature flags and kill switches around all new monolith-to-service integrations.
- Reduce the 30-minute maintenance dependency by proving online deployment procedures, connection draining, backward-compatible schema releases, and zero-downtime smoke tests.
    7. Implement integration, event, and data-transition patterns (depends on: 3, 5, 6)
       Create reusable patterns for safe coexistence between the monolith and services. This is the core mechanism for reversible migration without dual-write corruption.

- Introduce an event backbone and schema registry or equivalent governance, with versioned events, retention policies, dead-letter handling, replay procedures, and consumer ownership.
- Implement transactional outbox publishing in the monolith and each service. Events are committed with source data and delivered asynchronously with deduplication.
- Provide change-data-capture only where an outbox cannot initially be added, with monitoring and a time-bound plan to replace it.
- Build a replication and reconciliation framework that compares source and target counts, hashes, business totals, lag, and exception records.
- Standardise anti-corruption adapters so services do not inherit monolith-specific data shapes and semantics.
- Define transition states for each entity: monolith-owned, replicated read model, dual-read validation, service-owned with monolith compatibility adapter, and legacy-retired.
    8. Create quality, performance, and release assurance (depends on: 2, 4, 5, 7)
       Replace confidence based on a fortnightly monolith release with automated evidence for each independently deployed component. Focus first on revenue-critical and migration-affected flows.

- Build a production-like test environment with anonymised data, external-provider simulators, warehouse-file simulators, and repeatable country, currency, language, tax, and promotion test fixtures.
- Establish consumer-driven API and event contract tests. Producers may not release breaking changes until consumers have migrated or compatibility periods expire.
- Create end-to-end tests for browse-to-order, guest and registered checkout, payment success and failure, cancellation, return, refund, stock changes, loyalty, and back-office operations.
- Implement load, soak, spike, chaos, and failover tests using the observed 12x sale profile. Run them before every traffic expansion.
- Use shadow execution for high-risk decisions. Compare service and monolith outputs without changing customer outcomes.
- Set release gates for security, contracts, performance, observability, rollback rehearsal, and business reconciliation.
    9. Select and sequence extraction waves (depends on: 2, 3, 8)
       Prioritise small, low-coupling seams first, then use the resulting capabilities for harder domains. Pricing, promotions, checkout, and core order ownership are deliberately not first-wave candidates.

- Wave 1: edge routing, read-only catalogue API, search, and selected back-office read/reporting capabilities.
- Wave 2: inventory availability read model and warehouse integration adapter, while preserving the current order and stock authority initially.
- Wave 3: customer profile and selected loyalty read/write capabilities, subject to GDPR and identity constraints.
- Wave 4: order query model, notification or non-core order workflow, and returns workflow where process boundaries are confirmed.
- Wave 5: cart and checkout façade components, followed by payment-provider adapters only after reliability evidence is sufficient.
- Treat pricing and promotions as a dedicated discovery-and-modernisation stream. Extract only verified, bounded slices after exhaustive parity testing; retain the monolith engine behind an API if full extraction is not safe within 12 months.
- Define per-wave entry criteria, exit criteria, capacity allocation, and a no-go rule for work that would cross a sales protection window.
    10. Introduce edge routing and façade interfaces (depends on: 4, 5, 6, 8)
       Decouple channels from monolith internals before extracting business capabilities. Web, mobile, and back-office clients must use stable, versioned interfaces rather than service-specific implementation details.

- Place an API gateway or backend-for-frontend layer in front of existing endpoints without changing functional behaviour.
- Route by path, tenant/country, customer cohort, feature flag, and percentage of traffic. Default routing remains to the monolith.
- Add authentication, authorisation, rate limiting, request validation, response compatibility, correlation IDs, and traffic dashboards at the edge.
- Preserve mobile API compatibility through versioning and adapter endpoints. Do not force a mobile release as a prerequisite for backend extraction.
- Implement instant route rollback to the monolith, including tested handling for sessions, carts, cached responses, and in-flight requests.
- Measure baseline response equivalence and latency overhead before moving any business endpoint.
    11. Extract catalogue read API and modern search (depends on: 7, 8, 9, 10)
       Deliver the first customer-facing extraction through read-heavy capabilities with clear fallback paths. This validates the platform, routing, replication, and operational model without changing transaction ownership.

- Build a catalogue read service fed from monolith-owned catalogue data through outbox or controlled replication.
- Replace nightly-only Lucene rebuilding with an independently operated search service that supports incremental index updates, aliases, blue/green indexes, and rapid rollback to the existing index.
- Run catalogue and search in shadow mode. Compare product availability, locale content, ranking, facets, response time, and zero-result rates against current behaviour.
- Shift traffic gradually by country and cohort. Keep the monolith catalogue/search route live until parity and peak tests pass.
- Introduce cache policies, invalidation events, stale-data limits, and cache-bypass operational controls.
- Do not make the new search service authoritative for price or stock. It displays explicitly versioned read models from their owning domains.
    12. Modernise inventory integration and availability reads (depends on: 7, 8, 9, 10)
       Separate warehouse file exchange from customer-facing inventory reads while preserving warehouse and order-system correctness. Inventory changes are operationally sensitive and require explicit freshness semantics.

- Build a warehouse integration adapter that validates, records, deduplicates, and acknowledges inbound and outbound files without changing warehouse contracts initially.
- Publish inventory-change events and create an availability read model for storefront and search use.
- Define country and fulfilment-node stock semantics, safety-stock rules, oversell tolerance, freshness targets, and customer messaging for stale or unavailable stock.
- Shadow-compare the new availability result with the monolith for all products and warehouses. Reconcile every discrepancy before traffic expansion.
- Preserve current monolith stock reservation and allocation authority until order and inventory ownership boundaries are fully designed.
- Provide an immediate fallback to monolith availability reads and a replayable file-processing recovery process.
    13. Discover and contain pricing and promotions (depends on: 2, 6, 7, 8, 9, 10)
       Treat pricing and promotions as the highest-risk business capability. First make its behaviour observable and testable; do not attempt a big-bang rewrite based on incomplete knowledge.

- Form a dedicated cross-functional squad with senior engineers, merchandising, finance, country representatives, customer support, and QA.
- Inventory all rules, stored procedures, configuration tables, overrides, scheduled jobs, manual back-office actions, and country-specific exceptions.
- Capture real production decision inputs and outputs into a privacy-safe decision log. Build a golden-master corpus covering products, customer segments, countries, currencies, dates, carts, vouchers, stacking, tax, and edge cases.
- Put the existing engine behind a versioned pricing façade. New callers use the façade even while it delegates to monolith logic.
- Build a new rules-evaluation candidate service only for well-understood rule slices. Shadow-evaluate and compare exact price, discount, explanation, and latency before any customer exposure.
- Require business sign-off, discrepancy classification, and financial-impact analysis before moving each rule slice. Keep a per-slice route-back switch to the legacy engine.
    14. Extract customer and loyalty capabilities safely (depends on: 7, 8, 9, 10)
       Move customer-facing identity-adjacent data only after privacy, consent, and data ownership are clear. Avoid introducing inconsistent account state across countries and channels.

- Define the canonical customer identifier, consent model, data-retention rules, subject-access and deletion workflows, and access-control model.
- Start with a replicated customer profile read service, then migrate bounded profile writes through a façade with idempotency and audit trails.
- Move loyalty functions in small slices, such as balance inquiry before accrual or redemption, using a ledger model and reconciliation against legacy balances.
- Support account-session compatibility across web, mobile, monolith, and new services throughout the transition.
- Reconcile customer records, consent states, and loyalty balances daily during migration. Route exceptions to trained operations staff.
- Retain a compatibility adapter for legacy back-office functions until those workflows are migrated or retired.
    15. Extract order views and bounded post-order workflows (depends on: 7, 8, 9, 10, 14)
       Create independently deployable order-related value without prematurely splitting the transactional checkout path. Start with event-driven reads and post-order processes that can tolerate asynchronous integration.

- Publish reliable order lifecycle events from the monolith using the outbox pattern.
- Build an order query service for customer-service, customer self-service, notifications, and selected back-office views. Validate it against monolith order history and live state.
- Extract bounded workflows such as notifications, selected return initiation, return-status tracking, and non-financial order enrichment where ownership is explicit.
- Preserve monolith order creation, payment capture coordination, cancellation authority, refund authority, and warehouse order export until their transition design is approved.
- Implement reconciliation for order counts, states, refunds, returns, notification delivery, and event lag.
- Ensure every new order-facing view identifies source freshness and has a monolith fallback for support staff.
    16. Create cart, checkout, and payment transition architecture (depends on: 7, 8, 9, 10, 11, 12, 13, 15)
       Prepare the revenue-critical transactional path through façade-first migration, exhaustive provider testing, and progressive traffic control. This stage must not force immediate service ownership transfer.

- Define cart identity, guest-to-account merge rules, session persistence, currency and country transitions, promotion snapshots, inventory checks, and checkout idempotency keys.
- Introduce a checkout façade that initially delegates to the monolith. Route storefront and mobile gradually while maintaining response and error compatibility.
- Isolate each payment provider behind versioned adapters with token handling, webhook verification, idempotent authorisation and capture, retry policy, reconciliation, and provider-specific fallback behaviour.
- Build a payment ledger and daily reconciliation process covering authorisations, captures, refunds, chargebacks, provider settlements, and orders.
- Shadow-run checkout orchestration and payment-adapter decisions where possible. Use provider test environments and controlled internal cohorts before customer traffic.
- Do not split the final order-creation transaction until failure-mode analysis, compensating actions, support procedures, and sale-peak load tests demonstrate acceptable risk.
    17. Transfer ownership through controlled data cutovers (depends on: 7, 8, 11, 12, 13, 14, 15, 16)
       Move write ownership one entity group at a time after services have proven read parity and operational maturity. Each cutover is a reversible state transition, not a one-time database migration.

- For each entity, document source of truth, writer cutover sequence, replication direction, API consumers, data-retention obligations, reconciliation rules, and rollback point.
- Use expand-contract schemas, backfills with checksums, change capture or outbox replication, dual-read validation, and carefully bounded write cutovers.
- Avoid unrestricted dual writes. During transition, route writes through one command owner that publishes changes reliably to dependent systems.
- Reconcile continuously by identifiers, row counts, hashes, financial totals, stock totals, and business state transitions. Define thresholds that automatically halt traffic expansion.
- Retain legacy data read access and compatibility APIs until all consumers are migrated and the defined observation period has passed.
- Schedule any high-risk ownership cutover outside sales protection windows, with an approved rollback rehearsal and staffed hypercare period.
    18. Execute progressive traffic migration and rollback drills (depends on: 4, 8, 10, 11, 12, 13, 14, 15, 16, 17)
       Move production traffic only through measured, reversible increments. Every migration uses the same operational playbook regardless of domain.

- Progress through dark launch, shadow comparison, employee cohort, low-risk country or cohort, 1%, 5%, 25%, 50%, and full traffic stages where appropriate.
- Define quantitative promotion criteria for each stage: error rate, latency, conversion, search quality, price parity, payment approval rate, order completion, inventory discrepancy, support contacts, and reconciliation lag.
- Automate route rollback and validate it with game days. Rollback must restore a known compatible route without data loss or customer-visible duplicate operations.
- Run failure injection for dependency loss, event delay, duplicate messages, provider timeout, cache failure, database failover, and warehouse-file replay.
- Maintain staffed hypercare after each material expansion, with business, support, and engineering representatives able to pause or reverse rollout.
- Freeze traffic increases before sales protection windows. Use those windows only for monitoring, capacity verification, defect fixes with approved exceptions, and rehearsed rollback readiness.
    19. Prepare peak-season resilience and capacity certification (depends on: 4, 5, 8, 11, 12, 13, 16, 18)
       Certify both the hybrid estate and fallback paths for January and July sales. A service is not production-ready if its rollback target cannot sustain the traffic it might receive.

- Forecast peak demand by country, channel, page type, checkout step, payment provider, product launch, and warehouse activity.
- Load-test the full hybrid path at at least 12x observed baseline plus agreed headroom, including gateway, caches, monolith, services, event platform, databases, search, payment adapters, and warehouse integration.
- Test traffic reversion from each service to monolith and confirm that the monolith, database, and legacy search can absorb the reverted load.
- Establish capacity reservations, autoscaling limits, database connection limits, cache warm-up procedures, provider rate-limit agreements, and operational staffing plans.
- Conduct sale-day simulations, incident command exercises, communications rehearsals, and business-continuity tests with payment providers and warehouse stakeholders.
- Obtain formal peak-readiness sign-off from engineering, operations, commerce, finance, warehouse, and customer support.
    20. Retire legacy paths and establish steady-state service governance (depends on: 17, 18, 19)
       Conclude the 12-month programme by removing only proven-obsolete paths and making service ownership sustainable. Retain legacy components where removal would create unjustified business risk.

- Decommission monolith endpoints, batch jobs, Lucene components, table access paths, and stored procedures only after consumer inventory, data archival, reconciliation, and rollback-retention periods are complete.
- Remove temporary replication and compatibility adapters in controlled releases. Update runbooks, diagrams, recovery procedures, and ownership records at the same time.
- Measure and reduce residual monolith coupling, direct database access, synchronous dependency chains, event lag, and operational toil.
- Establish quarterly architecture reviews, API and event lifecycle governance, service scorecards, dependency upgrade policies, resilience testing, and disaster-recovery exercises.
- Conduct a post-migration review against business outcomes, incident history, delivery lead time, cost, and peak performance. Prioritise any remaining pricing, checkout, order, or database decomposition work as a funded follow-on roadmap.
- Confirm that independently deployable services have named owners, on-call coverage, SLOs, operational documentation, and tested rollback or recovery procedures.

  Previous Proposal 3 (ID: b39f2a46-559f-4920-9664-540fcdb612f4, Agent: grok-4.6_initial_3, LLM: xai/grok-4.6):
    Estimated Complexity: high
    Success Metrics: - Zero unplanned downtime attributed to migration work across the 12 months.
- Every production cutover has a practised rollback that restores the previous path in minutes.
- January and July peak capacity at or above today's 12x headroom, with no extra error-budget burn on search, cart, checkout or payments.
- Feature throughput stays at or above the current two-week train; no programme-wide feature freeze.
- At least search, catalogue, identity, inventory, pricing, cart, checkout and OMS deploy independently of the monolith artefact.
- Dual-run mismatch rate for prices and stock below an agreed threshold before each traffic shift (target: 0 on money paths).
- Golden-journey pass rate 100% on critical paths before and after each cutover.
- Monolith database coupling reduced: no new cross-context joins; stored-procedure call volume on extracted domains at zero after ownership transfer.
- Mean time to revert a bad service release under 10 minutes via flags or routing.
    Steps (25):
    1. Charter, governance and non-negotiables
       Write a short **migration charter** that product, ops, finance and all five teams sign.

Feature work never stops. Only production risk is constrained.

- Name one accountable migration lead and a weekly steering forum.
- Ban big-bang rewrites, shared-database-first splits and un-reversible cutovers.
- Require a tested rollback for every production step.
- Keep the two-week monolith release train for features until a domain is fully extracted.
    2. Peak calendar and freeze protocol (depends on: 1)
       Protect **January and July** sales with hard engineering blackouts.

No extractions, schema splits or traffic switches in the six weeks before a sale or the two weeks after, unless they are already proven and idle.

- Publish the 12-month calendar in week one.
- Freeze means no new migration risk, not a feature freeze.
- Require a peak capacity rehearsal before each blackout.
- Give ops a veto on any change that could affect checkout, payments, stock or search.
    3. Baseline architecture, data and SLOs (depends on: 1)
       Measure the live system before changing it.

Build a factual map of the 2M-line monolith, the 1.2 TB database and the real traffic shape.

- Trace the top 30 user journeys and the 350 tables they touch.
- Record p50/p95/p99, error rates and 12x peak headroom per journey.
- Inventory stored procedures, cross-module joins and file exchanges.
- Tag every endpoint used by the storefront, mobile app and back-office.
    4. Delivery platform, flags and progressive delivery (depends on: 1)
       Give every team a **safe way to ship** without the 30-minute maintenance window.

New work deploys behind flags. Old work stays on the existing train until it is ready.

- Add feature flags, weighted routing and instant revert at the edge.
- Build CI that can later publish one artefact per service.
- Keep Java 8 on the monolith. Start new services on a current LTS.
- Provide preview environments that replay production-like traffic.
    5. Observability and error budgets (depends on: 3, 4)
       Instrument the monolith as if it were already many services.

You cannot extract what you cannot see.

- Add distributed tracing, RED metrics and structured logs with correlation IDs.
- Define SLOs for search, PDP, cart, checkout, payments and back-office.
- Page on error-budget burn, not on CPU.
- Dashboards must show monolith vs new service side by side for every cutover.
    6. Safety net: journeys, contracts and load (depends on: 3)
       Raise the net where extraction will cut.

Unit coverage at 25% is not enough. Protect behaviour, not lines.

- Record golden journeys for browse, price, cart, checkout, order, return and loyalty.
- Add contract tests on every mobile and storefront endpoint.
- Capture characterization tests around stored procedures before moving them.
- Automate a 12x peak load test and run it before each sale and each major cutover.
    7. Bounded contexts and extraction backlog (depends on: 3)
       Draw domain boundaries from the business, not from the package tree.

Sequence work by **risk and coupling**, not by fashion.

- Contexts: identity, catalogue, search, pricing, inventory, cart, checkout, orders, returns, loyalty, back-office.
- Extract read-mostly and already-async seams first (search, inventory files).
- Leave pricing and checkout until dual-run and reconciliation exist.
- Rank a 12-month backlog with a rollback story on every item.
    8. Team operating model without a freeze (depends on: 1, 7)
       Keep five domain teams. Stop treating the repo as a single ownership blob.

Each team ships features in the monolith **and** prepares its future service.

- Assign a service to own per team, plus a shared platform pair.
- Code owners and module walls inside the current repository first.
- A small platform group owns gateway, flags, events, CI and data tooling.
- Product still plans features; migration work is a percentage of each sprint, not a separate freeze.
    9. Modularise the monolith in place (depends on: 6, 7)
       Create seams before you create processes.

New code may not add cross-module joins or new stored-procedure coupling.

- Split packages by bounded context with compile-time walls.
- Replace in-process calls at boundaries with interfaces (branch by abstraction).
- Document and freeze the worst pricing and checkout internals; wrap them.
- Ban new features from reaching into another team's tables.
    10. Strangler facade and instant traffic rollback (depends on: 4, 5)
       Put a reverse proxy in front of every public and mobile endpoint.

Clients keep the same URLs. You choose monolith or service per route and per percentage.

- Preserve headers, sessions, cookies and the four languages.
- Shadow traffic before any live percentage.
- Rollback is a route change, not a redeploy, and must complete in minutes.
- Storefront SSR and the mobile app stay compatible until a later BFF if needed.
    11. Events, outbox and CDC backbone (depends on: 5, 9)
       Give the monolith a **reversible integration spine**.

Services must not call each other's databases. They subscribe to facts.

- Add an outbox in the same Postgres transaction as business writes.
- CDC from the monolith for tables you do not yet own.
- Standard event names for product, price, stock, customer, order and return.
- Idempotent consumers and a dead-letter process before the first extraction.
    12. Data-change playbook: dual-write, reconcile, roll back (depends on: 11)
       Treat every data move as a campaign with an abort switch.

The 1.2 TB database stays the system of record until a service proves otherwise.

- Dual-write with the monolith write winning on conflict during trial.
- Nightly and continuous reconciliation with row-level diffs.
- Never cut stored procedures until logic has an equivalent test harness.
- Rollback means stop writes to the new store and keep serving from Postgres.
    13. Extract search as the first service (depends on: 2, 8, 10, 11, 12)
       Replace the nightly Lucene rebuild with an independently deployed **search service**.

This is read-heavy, already eventually consistent, and off the payment path.

- Index from catalogue and price events, not from a nightly dump.
- Shadow queries against current Lucene until precision/recall match.
- Shift traffic 1% → 10% → 50% → 100% with instant route rollback.
- Keep the old index warm through the next sale as a cold standby.
    14. Extract catalogue read models (depends on: 13)
       Serve product, media and localisation from a catalogue service.

Writes can stay in the monolith until editors have a new path.

- Build country and language-specific read models for eight markets.
- Keep one product identity so pricing, stock and search stay aligned.
- Cut storefront and mobile read traffic via the strangler.
- Do not move merchandising tools until reads are stable.
    15. Extract identity, accounts and session (depends on: 8, 10, 12)
       Pull login, profile, addresses and session behind a dedicated service.

Mobile and web keep the same auth cookies or tokens during the switch.

- Migrate sessions without forced logouts.
- Dual-read loyalty points until that domain is extracted.
- GDPR/export and deletion flows must work in both systems.
- Rollback restores monolith auth with no password resets.
    16. Extract inventory and warehouse sync (depends on: 8, 11, 12)
       Replace the 15-minute file exchange with an inventory service that still talks to the warehouse.

The warehouse interface stays file-based until they can change. Your side becomes events.

- Service owns ATP, reservations and oversell rules.
- Adapter keeps the existing file contract so warehouse risk is zero.
- Cart and checkout read stock from the service via API or replica.
- Prove no extra oversell versus today's 15-minute lag before a sale.
    17. Pricing archaeology and dual-run harness (depends on: 6, 9)
       Do not extract the 200k-line pricing module until you can prove equivalence.

Nobody fully understands country rules. Tests must become the spec.

- Capture production price traces for all eight countries and three currencies.
- Build a harness that replays promotions, baskets and edge SKUs.
- Freeze behavioural snapshots; new promo features implement twice until cutover.
- Only then wrap pricing behind an interface inside the monolith.
    18. Extract pricing and promotions behind dual-run (depends on: 14, 17, 12)
       Run the new pricing service in **shadow** until it matches the monolith on live baskets.

Checkout keeps using monolith prices until the error budget is clean.

- Compare every quote; alert on any currency, tax or promo mismatch.
- Shift read traffic first, then write of promo usage.
- Keep the monolith engine deployable as rollback through the next two sales.
- Country-specific rules move last, one market at a time if needed.
    19. Extract cart (depends on: 15, 16, 18)
       Move the cart after identity, catalogue, stock and price reads are stable.

Cart is stateful. Lose no baskets during cutover.

- Dual-write carts; reconcile abandoned and active baskets.
- Preserve promo application using the dual-run price API.
- Session migration must survive app versions in the wild.
- Rollback reattaches baskets to the monolith cart tables.
    20. Extract checkout and payment orchestration (depends on: 19)
       Strangle checkout without touching the three payment providers in one step.

A thin orchestration service talks to existing provider integrations first.

- Keep PCI and provider contracts stable; wrap, do not rewrite.
- Idempotent order placement with an outbox to OMS.
- Canary by country and by payment method.
- Rollback is route-plus-flag; in-flight payments complete on the old path.
    21. Extract order management (depends on: 20)
       Move post-purchase order state once checkout emits reliable events.

OMS must survive 12x peaks and warehouse files.

- Order of record shifts only after reconciliation is clean for a full weekly cycle.
- Back-office screens can still read a projection while writes move.
- Returns and finance reports stay correct during dual-run.
- Keep monolith OMS as standby through one sale after cutover.
    22. Extract returns, loyalty and remaining back-office (depends on: 15, 21)
       Peel remaining domains once orders and identity are independent.

Staff of 300 must not get a big-bang UI change.

- Returns service consumes order events and drives refunds via payment facade.
- Loyalty becomes the owner of points with dual-write from checkout.
- Back-office gets BFFs or modular UIs per domain, not a new monolith.
- Train staff per screen group; keep old screens until the new ones match.
    23. Split data ownership and retire stored procedures (depends on: 16, 18, 21)
       Give each stable service its **own schema or database** only after traffic and reconciliation are boring.

Shared Postgres is allowed during transition. It is not the end state.

- Move table ownership context by context; no cross-service joins.
- Rewrite stored procedures into service code with the characterization harness.
- Shrink the 1.2 TB monolith database as tables go dark.
- Rollback is restoring replication to the monolith copy, practised in staging.
    24. Independent deploy pipelines and repository split (depends on: 8, 23)
       When a service is independently releasable, stop bundling it into the fortnightly artefact.

Teams ship on demand with automated checks and progressive delivery.

- One pipeline per service: test, canary, promote, revert.
- Split repos only after module walls and CI already work in the monorepo.
- Contract tests gate consumer and provider deploys.
- The remaining monolith keeps the old two-week train until it is small.
    25. Peak rehearsals, chaos and residual shrink (depends on: 2, 22, 24)
       Prove **12x capacity** on the mixed architecture before each sale.

Then keep shrinking the monolith so it is a shell, not a risk.

- Game-days: provider failure, CDC lag, flag revert, search fallback, stock file delay.
- Scale tests on checkout, search and inventory with production-sized data.
- Delete dead modules, jobs and tables only after two quiet weeks.
- End state: independently deployable services, instant rollback still in place, no unplanned downtime attributed to the programme.

  Previous Proposal 4 (ID: 9219a465-1887-4823-81a8-3cbfc3bb545b, Agent: deepseek-v4-pro_initial_4, LLM: deepseek/deepseek-v4-pro):
    Estimated Complexity: high
    Success Metrics: - 100% of critical customer journeys remain within SLOs during migration; no unplanned downtime outside planned windows.
- Every extraction step has a rehearsed rollback path that restores monolith behaviour in under 5 minutes.
- Peak-season capacity maintained: January and July sales complete without capacity-related errors, with peak traffic at least 12x baseline and error rate <= 0.1%.
- By month 12, at least 8 core business capabilities are deployed as independently deployable services from separate repositories with separate data ownership.
- Monolith code is reduced by at least 60%, and the remaining monolith no longer owns migrated data or executes migrated stored procedures.
- Deployment frequency increases from one release every two weeks to daily per service; lead time for changes decreases from weeks to hours.
- Test coverage on changed code reaches at least 80%; critical pricing and checkout paths have contract and parity tests.
- Zero data loss or irreversible data corruption during migration; reconciliation discrepancies are below 0.01% of records.
- Feature delivery velocity remains at least equal to pre-migration levels; no feature freeze is imposed.
- No cross-service direct database joins remain for migrated capabilities; all service data access happens through APIs or events.
    Steps (19):
    1. Baseline and decompose the monolith into bounded contexts
       Capture the current behaviour, data model, and operational risks before changing anything. The output is a shared map that justifies every later cutover.
- Inventory all modules, endpoints, database tables, stored procedures, cross-module joins, external integrations, and batch jobs.
- Map business capabilities to bounded contexts and identify candidate service seams and data owners.
- Record every country/currency/language variation, especially the 200k-line pricing and promotions module.
- Capture the peak-season calendar, current deployment windows, known failure modes, and rollback mechanisms.
- Create a risk register with blast radius and rollback criteria for each candidate extraction.
    2. Define target service architecture and migration sequence (depends on: 1)
       Agree the target state and the guardrails before building any new service.
- Publish target decomposition: storefront, catalogue/search, pricing/promotions, cart/checkout, orders, inventory, customers/loyalty, returns, back-office.
- Define synchronous APIs, asynchronous events, idempotency, retries, sagas, and eventual consistency where required.
- Define data ownership and database-per-service strategy; prohibit cross-service joins and direct access to another service's tables.
- Define API versioning, security, tenancy, and country-specific routing.
- Choose migration sequence: start with low-risk read-heavy capabilities and delay peak-sensitive cutovers until outside sales windows.
- Set the rollback requirement: every change must be behind a flag or reversible migration with rehearsed rollback.
    3. Establish observability, SLOs and production load testing (depends on: 1)
       Make the current system measurable so cutovers are based on data, not hope.
- Add structured logs, metrics, and distributed tracing to the monolith and future services.
- Define SLOs and error budgets for storefront, catalogue, cart, checkout, payments, and order management.
- Add synthetic transactions and real-user monitoring for 8 countries, 3 currencies, and 4 languages.
- Build a performance test environment that replays production-like traffic at peak 12x volume.
- Create dashboards for golden signals, slow queries, stored procedure hotspots, and cache/index health.
    4. Build zero-downtime CI/CD and database migration automation (depends on: 2)
       This is the safety rail for every later step: frequent, reversible, low-risk deployments.
- Replace the biweekly single-artifact release with a pipeline supporting per-service builds, automated tests, security scans, and deployment.
- Introduce canary and blue-green deployment with automated rollback based on SLOs and error budgets.
- Add expand/contract database migration patterns: first add new schema, dual-write or synchronise, switch reads, then remove old schema in a later release.
- Ensure every service change is independently deployable in minutes, with no planned maintenance window.
- Use infrastructure-as-code and immutable artifacts for all environments.
    5. Strengthen tests and add contract testing before cutting seams (depends on: 3, 4)
       Raise confidence in behaviour without freezing features, focusing on seams to be extracted.
- Build characterization tests around current API endpoints, stored procedures, pricing rules, and checkout flows.
- Add consumer-driven contract tests between the monolith and new services.
- Introduce mutation testing and enforce at least 80% coverage on changed code.
- Add data-migration tests, reconciliation tests, and performance regression gates to CI/CD.
- Keep a long-running dual-read and diff harness for later services.
    6. Introduce traffic routing and feature flag platform (depends on: 4, 5)
       Enable gradual migration and instant rollback without redeploying the entire monolith.
- Deploy a feature flag system and edge/API gateway that can route traffic by customer, country, currency, language, percentage, and header.
- Add dark-launch capability to send shadow traffic to new services while the monolith remains source of truth.
- Implement kill switches that revert to monolith paths in one action.
- Integrate flags with SLO dashboards and deployment rollback.
    7. Extract customer accounts and loyalty as pilot service (depends on: 2, 3, 4, 5, 6)
       Prove the extraction playbook on a well-bounded, lower-risk capability before touching the most complex modules.
- Create a customer service owning customer, address, and loyalty data; expose a REST API with the same contracts.
- Move related monolith code behind an anti-corruption layer; run dual-writes or CDC to keep data in sync.
- Use expand/contract database migration: retain monolith tables temporarily, synchronise with the service, then switch reads/writes by flag.
- Launch to a small country and a small traffic percentage; monitor SLOs and rollback if errors exceed the error budget.
- Use the pilot to refine templates, runbooks, and training for other teams.
    8. Extract catalogue and search into a dedicated service (depends on: 3, 4, 5, 6, 7)
       Move the read-heavy catalogue and search path first, as it is valuable and relatively safe if done in shadow mode.
- Build a catalogue/search service that owns product, category, and search data; maintain the Lucene index within the service or via a dedicated index.
- Synchronise catalogue data from the monolith through CDC or events; stop cross-module joins.
- Serve storefront and mobile via the new catalogue/search API; run shadow reads against the monolith and compare.
- Route reads progressively by country and language and validate search quality, latency, and conversion.
- Keep the monolith fallback and flag-based rollback until after the peak readiness gate.
    9. Extract pricing and promotions with dual-run comparison (depends on: 7, 8)
       The most complex module; migration must be based on observed behavioural equivalence.
- Build a pricing/promotions service with country-specific rules as versioned configuration or domain rules.
- Run the new service in shadow mode on all checkout/cart/catalogue calls and compare every calculation with the monolith for months before cutover.
- Treat any divergence as a defect; require 100% parity on sampled and historical promotion scenarios before routing live traffic.
- Expose a pricing API and route live reads/writes only by country and promotion type, with immediate rollback.
- Keep the monolith promotion engine available until after all peak seasons.
    10. Extract inventory service and modernise warehouse integration (depends on: 7)
       Replace the 15-minute file exchange with safer, event-driven inventory updates while keeping the old path as fallback.
- Build an inventory service owning stock levels, reservations, and warehouse sync logic.
- Integrate with the warehouse system via API or events and keep the file exchange running in parallel for dual sync.
- Expose inventory availability and reservation APIs for cart, checkout, and back-office.
- Run reconciliation between the old file batch and the new event flow for all SKUs; eliminate divergence before cutover.
- Route inventory consumers to the service progressively, maintaining the monolith fallback.
    11. Extract cart and checkout service (depends on: 7, 8, 9, 10)
       Move the highest-value transaction path only after its dependencies are available and proven.
- Build a cart/checkout service that owns cart state and checkout workflow; it calls customer, catalogue, pricing, and inventory APIs with fallbacks.
- Integrate the three payment providers through adapters; implement idempotency, retries, and reconciliation.
- Use saga or orchestration for payment, inventory reservation, and order creation.
- Route by country, currency, and traffic percentage; start with one payment provider and one country.
- Rehearse rollback to monolith checkout and validate that no cart or payment is lost.
    12. Peak readiness gate before first sales peak (depends on: 7, 8, 9, 10, 11)
       Protect the first peak by freezing risky cutovers while allowing normal feature work through flags.
- Freeze new service cutovers and irreversible data migrations for four weeks before and during the peak.
- Run production-like load tests at 12x baseline with monolith and new services in their current routing ratios.
- Rehearse rollback for every extracted service and confirm the monolith fallback handles full load.
- Pre-scale infrastructure to at least 30% above expected peak.
- Keep on-call and war-room runbooks ready; certify only if all SLOs pass in load tests.
    13. Extract order management service after first peak (depends on: 11, 12)
       Move order persistence and lifecycle after the first peak, using events from checkout and inventory.
- Build an order service owning orders and order lines; consume order-placed events from checkout and payment.
- Replace monolith order creation and status update code behind flags.
- Backfill historical orders into the service and run reconciliation.
- Route order read/write traffic progressively; maintain the monolith fallback.
- Ensure returns and customer service integration remains consistent.
    14. Extract returns service (depends on: 13)
       Move returns and refunds out of the monolith once order and inventory services are stable.
- Build a returns service owning return requests, labels, refund settlements, and status.
- Integrate with order, inventory, and payment services via APIs and events.
- Migrate business rules country-by-country with dual-run comparison.
- Keep the monolith fallback and rollback for all return journeys.
    15. Extract back-office capabilities (depends on: 13, 14)
       Decouple internal staff tools from the monolith so operations run on the new services.
- Build a new back-office UI or modular back-office services that use the same APIs as storefront services.
- Migrate catalogue, pricing, inventory, order, customer, and returns management screens incrementally.
- Route the 300 staff users per function and country; keep the old monolith back-office available during transition.
- Automate manual processes and remove direct SQL access to migrated data.
    16. Peak readiness gate before second sales peak (depends on: 13, 14, 15)
       Repeat the readiness gate for the second peak with more services in production.
- Freeze cutovers and irreversible migrations four weeks before and during the peak.
- Run full production-like load test at 12x peak with current routing ratios.
- Rehearse rollback for order management, returns, and back-office services.
- Verify capacity headroom and failover to monolith if required.
- Certify only if all critical SLOs are met under peak stress.
    17. Decommission monolith code and data ownership for migrated modules (depends on: 16)
       Remove the old implementation only after both peaks and after all services have proven parity.
- Remove feature flags and dark-launch paths for customer, catalogue, pricing, inventory, cart/checkout, orders, and returns.
- Drop or archive monolith tables and stored procedures for migrated modules after data reconciliation.
- Delete monolith code paths and reduce monolith size; verify no cross-module joins remain.
- Run final reconciliation and zero-downtime data archival.
    18. Final cutover, contract freeze and monolith retirement (depends on: 17)
       Complete the strangler migration so the monolith stops serving business traffic.
- Switch any remaining traffic to services and remove edge routing to the monolith.
- Decommission monolith deployments; maintain a read-only archive for audit.
- Finalise API contracts, versioning, and service ownership.
- Complete security reviews and production runbooks for all services.
    19. Continuous improvement and governance handover (depends on: 18)
       Make the new architecture the default and keep it safe after migration.
- Embed service ownership: each team owns build, deploy, and operate with SLOs.
- Review cost, performance, and reliability; tune autoscaling and capacity for the next peak.
- Update architecture decision records, runbooks, and onboarding material.
- Establish a light-touch governance board for API and data contracts.

  Previous Proposal 5 (ID: 733858cb-4dad-4bb6-85c9-79a3e6d968d9, Agent: qwen3.8-max_initial_5, LLM: alibaba/qwen3.8-max):
    Estimated Complexity: high
    Success Metrics: - Zero unplanned downtime throughout the 12-month migration; all maintenance performed via blue-green or canary cutover.
- Every extraction step is reversible within 5 minutes via feature-flag rollback, validated at least once in production before full cutover.
- Deployment frequency increases from bi-weekly to at least daily per service by month 4.
- Peak-load capacity sustained at 12× normal traffic (≈ 480 000 orders/day) with p99 latency ≤ 800 ms for checkout and ≤ 400 ms for storefront during January and July sales.
- Test coverage on all migrated code paths reaches ≥ 80 %; contract tests exist for every inter-service boundary.
- Monolith codebase reduced from 2 M lines to 0 lines in production by end of month 12.
- All 350 tables are owned by exactly one service; zero cross-service direct database joins remain.
- The three payment providers maintain ≥ 99.95 % successful transaction rate throughout the migration.
- Back-office availability for 300 staff ≥ 99.9 % during business hours across all 8 countries.
- Mean time to recovery (MTTR) for any single-service incident ≤ 10 minutes.
- No degradation in order-accuracy rate (≥ 99.99 %) or inventory reconciliation accuracy (≥ 99.9 %) at any point during the migration.
- Customer-facing error rate (5xx) stays below 0.1 % across all 8 countries, 3 currencies, and 4 languages throughout the programme.
    Steps (20):
    1. Full-Scope Discovery and Dependency Mapping
       Perform a **complete technical and organisational audit** of the monolith before any code changes.

- Run static-analysis tools (jQAssistant, ArchUnit, custom SQL scripts) to produce a module-level dependency graph across all 2 M lines of Java and all 350 PostgreSQL tables.
- Catalogue every stored procedure, trigger, and cross-module join; classify each as *local*, *cross-module read*, or *cross-module write*.
- Interview each of the five teams to document tribal knowledge, especially the pricing & promotions rules (200 K lines, country-specific logic).
- Map all external integrations: three payment providers, warehouse file exchange, mobile-app endpoints, back-office UI routes.
- Record current performance baselines: p50 / p95 / p99 latency per endpoint, throughput, DB query plans for the top-100 queries.
- Deliverable: a living architecture dossier stored in a shared wiki, updated throughout the migration.
    2. Build CI/CD Pipelines and Feature-Flag Platform (depends on: 1)
       Create the **deployment and release-safety infrastructure** that every later step depends on.

- Stand up a CI/CD stack (e.g. GitLab CI or GitHub Actions → ArgoCD) capable of building, testing, and deploying individual modules independently.
- Introduce a feature-flag platform (LaunchDarkly, Flagsmith, or Unleash) wired into the monolith via a thin SDK; every new or changed code path ships behind a flag.
- Define branching strategy: one repo per future service, plus the existing monorepo during the transition period.
- Automate canary and blue-green deployment patterns so every release can be rolled back in under five minutes.
- Target: reduce the two-week release cycle to **daily deployable** by end of this step.
    3. Establish Observability, Tracing, and SLO Baseline (depends on: 1)
       Instrument the monolith so that **every subsequent extraction is measurable** and regressions are caught within minutes.

- Deploy OpenTelemetry agents across all application nodes; export traces, metrics, and structured logs to a central stack (Grafana Tempo + Prometheus + Loki, or Datadog).
- Define SLOs per domain: storefront p99 < 400 ms, checkout p99 < 1.2 s, search p95 < 300 ms, back-office p95 < 2 s.
- Build real-time dashboards per SLO with alerting thresholds; wire alerts to on-call rotation.
- Implement synthetic transaction monitoring covering the critical user journeys (browse → cart → checkout → payment → confirmation) across all 8 countries, 3 currencies, and 4 languages.
- Establish an error-budget policy: any extraction step that breaches its SLO budget is automatically rolled back.
    4. Automated Testing Uplift and Contract-Test Foundation (depends on: 2)
       Raise test coverage from **25 % to at least 60 %** on the paths that will be touched first, and introduce contract testing.

- Use mutation testing (PIT) to identify the highest-risk untested paths; prioritise checkout, payment, and inventory flows.
- Add integration tests using Testcontainers with a seeded copy of the production schema.
- Introduce Pact (or Spring Cloud Contract) for consumer-driven contract tests between every pair of modules that will become separate services.
- Build a regression suite of end-to-end smoke tests runnable in < 15 minutes, executed on every deploy.
- Define a policy: no extraction proceeds unless the affected module reaches the agreed coverage threshold.
    5. Team Topology Realignment and Governance Model (depends on: 1)
       Reorganise the five teams into **stream-aligned, domain-owned squads** and agree on governance rules for the migration.

- Map each team to a bounded context: (1) Storefront & Search, (2) Pricing & Promotions, (3) Cart, Checkout & Payments, (4) Order Management, Inventory & Returns, (5) Customer, Loyalty & Back-Office.
- Assign a Platform/Enablement guild (2–3 senior engineers drawn across teams) responsible for shared infra, libraries, and cross-cutting concerns.
- Agree on API governance: versioning policy (URL-path major, header minor), deprecation window (minimum 90 days), and an internal API catalogue.
- Set up a weekly cross-team architecture sync and a migration-risk register reviewed every sprint.
- Define the rollback decision tree: who can trigger a rollback, under what SLO breach, and the communication protocol.
    6. Strangler-Fig Gateway and Anti-Corruption Layer (depends on: 2, 3)
       Deploy an **API gateway in front of the monolith** that will route traffic to either the legacy code or the new services, enabling incremental extraction.

- Place a reverse-proxy / service mesh layer (e.g. Kong, Envoy via Istio, or AWS ALB + App Mesh) in front of the existing load balancer.
- Implement an Anti-Corruption Layer (ACL) service that translates between the monolith's internal models and the new service APIs.
- Configure the gateway to route by URL pattern, header, or feature flag; default route goes to the monolith.
- Support traffic mirroring (shadow traffic) so new services can be validated against live production traffic before receiving real requests.
- All mobile-app and back-office traffic passes through the gateway from day one; server-rendered pages are proxied transparently.
    7. Database Decomposition Strategy and Shared-Data Refactor (depends on: 1, 4)
       Prepare the **1.2 TB PostgreSQL database** for eventual per-service ownership without a big-bang migration.

- Classify all 350 tables by bounded context using the dependency map from S1.
- Eliminate cross-module joins at the application layer first: replace them with service calls or denormalised read models.
- Convert stored procedures that span contexts into application-level logic behind the ACL; keep single-context procedures temporarily.
- Introduce an internal event log (outbox pattern) on the existing database: every state change publishes a row to an `outbox` table, later relayed to a message broker.
- Define the target data-ownership matrix: which service will own which tables, and which data will be replicated read-only.
- Plan a dual-write / change-data-capture (CDC) strategy using Debezium so that during transition both old and new stores stay consistent.
    8. Event-Driven Backbone and Async Messaging Layer (depends on: 6, 7)
       Stand up the **messaging infrastructure** that decouples services and replaces synchronous cross-module calls.

- Deploy Apache Kafka (or AWS MSK) with topics per bounded context: `catalogue-events`, `order-events`, `inventory-events`, `pricing-events`, `customer-events`.
- Implement the transactional outbox relay (Debezium → Kafka Connect) so the monolith can publish domain events without code changes to business logic.
- Define event schemas in a central Schema Registry (Avro / Protobuf) with backward-compatibility enforcement.
- Add idempotent consumer patterns and dead-letter queues from day one.
- Validate throughput: the backbone must sustain 12× peak (≈ 480 000 orders/day equivalent event volume) with headroom.
    9. Containerisation and Kubernetes Platform Readiness (depends on: 2, 3)
       Package the monolith and prepare a **Kubernetes-based runtime** for all future services.

- Dockerise the existing monolith (multi-stage build, slim JRE image) and deploy it to a Kubernetes cluster alongside the gateway.
- Provision namespaces per bounded context, with network policies enforcing that only the gateway and the ACL can reach the monolith.
- Configure horizontal pod autoscaling, pod disruption budgets, and resource quotas sized for 12× peak.
- Set up a service mesh (Istio or Linkerd) for mTLS, traffic splitting, circuit breaking, and retry policies.
- Run a load test replicating the January-sale profile (12× normal traffic) to validate the platform before any service extraction.
    10. Extract Customer Accounts and Loyalty Service (Wave 1) (depends on: 4, 6, 7, 8, 9)
       Carve out the **lowest-risk, well-bounded domain** first to validate the full extraction playbook.

- Build a new `customer-service` (Java 21 / Spring Boot 3 or Kotlin) exposing REST + gRPC APIs for registration, authentication, profile, and loyalty points.
- Migrate the relevant 15–20 tables to a dedicated PostgreSQL instance using the CDC dual-write pattern from S7.
- Place the service behind the ACL; route traffic via feature flags starting at 1 % → 10 % → 50 % → 100 % over two weeks.
- The monolith continues to serve as fallback; a single flag flip routes 100 % back.
- Validate contract tests, SLO dashboards, and rollback procedure end-to-end.
- This extraction serves as the **reference implementation** for all subsequent waves.
    11. Extract Catalogue and Search Service (Wave 2) (depends on: 10)
       Replace the nightly Lucene rebuild with a **real-time search and catalogue service**.

- Build a `catalogue-service` owning product data, categories, and media references; use CDC from the monolith DB during transition.
- Replace Lucene with Elasticsearch or OpenSearch; index updates driven by Kafka events instead of the nightly batch.
- Expose search and browse APIs through the gateway; server-rendered storefront pages call the new API via the ACL.
- Migrate in two sub-phases: (a) read-only catalogue and search behind flags, (b) write path (product updates from back-office) once reads are stable.
- Keep the legacy Lucene index warm for instant rollback for 60 days.
- Validate that search latency meets the p95 < 300 ms SLO across all 4 languages.
    12. Extract Inventory and Warehouse Sync Service (Wave 3) (depends on: 10)
       Isolate the **inventory domain and its 15-minute file-exchange** with the warehouse system.

- Build an `inventory-service` owning stock levels, reservations, and warehouse synchronisation.
- Replace the file-based exchange with an event-driven adapter: the service consumes warehouse updates via SFTP poll or API and publishes `inventory-updated` events to Kafka.
- During transition, run the adapter in parallel with the legacy file job; reconcile counts nightly.
- Checkout and order-management modules consume inventory availability via synchronous gRPC (with circuit breaker) and asynchronous events for reservation confirmations.
- Migrate stock tables using CDC; rollback path re-points reads to the monolith tables.
- Validate under 12× peak load: inventory checks must not become a bottleneck during flash sales.
    13. Deep Analysis and Rule Documentation for Pricing & Promotions (depends on: 1)
       Before touching the **most complex 200 K-line module**, invest in understanding and documenting its rules.

- Pair domain experts from each of the 8 country teams with developers to walk through every pricing rule, promotion type, and country-specific override.
- Produce a machine-readable rule catalogue (decision tables or a lightweight DSL) that captures all 200+ identified rules.
- Identify dead code, redundant branches, and rules that have not fired in the last 24 months (use production logging and feature-flag data).
- Classify rules into: (a) universal, (b) country-specific, (c) campaign/temporary.
- Define the target architecture: a `pricing-service` with a rules engine (Drools, Easy Rules, or a custom evaluation pipeline) externalised from application code.
- Deliverable: a signed-off rule specification document that all five teams agree represents current behaviour.
    14. Extract Pricing and Promotions Service (Wave 4) (depends on: 11, 12, 13)
       Rebuild the **highest-risk module** as an independent service using the documented rule set from S13.

- Build a `pricing-service` with a pluggable rules engine; encode the rule catalogue from S13 as configuration rather than hard-coded Java.
- Expose two API surfaces: synchronous price calculation (called by cart/checkout) and asynchronous promotion evaluation (event-driven for campaign changes).
- Run the new service in **shadow mode** for 4–6 weeks: every pricing request is sent to both the monolith and the new service; a comparator flags discrepancies.
- Only after the discrepancy rate drops below 0.01 % over two full weeks (including a weekend) begin traffic shifting via feature flags.
- Migrate pricing tables via CDC; keep monolith pricing logic compilable and deployable as rollback for 90 days.
- Assign dedicated on-call coverage for the first 30 days post-cutover.
    15. Extract Cart, Checkout, and Payment Service (Wave 5) (depends on: 14)
       Separate the **revenue-critical checkout flow** into its own service with hardened payment integration.

- Build a `checkout-service` owning cart state, checkout orchestration, and integration with the three payment providers.
- Cart state moves to a dedicated data store (Redis for transient cart, PostgreSQL for persisted orders) with CDC from the monolith during transition.
- Payment-provider integrations are wrapped in an adapter layer with circuit breakers and idempotency keys; failover order between providers is configurable per country.
- Migrate in sub-phases: (a) cart operations, (b) checkout orchestration, (c) payment capture and confirmation.
- Run chaos-engineering tests (payment-provider timeout, partial failure) before enabling real traffic.
- Rollback: feature flag routes checkout back to monolith; in-flight transactions are drained gracefully.
    16. Extract Order Management and Returns Service (Wave 6) (depends on: 15)
       Move **post-purchase order lifecycle and returns processing** into a dedicated service.

- Build an `order-service` consuming `order-placed` events from checkout; it owns order state machine, fulfilment tracking, and returns workflow.
- Integrate with the inventory service for reservation release and with the warehouse adapter for shipping updates.
- Back-office order views call the new service API through the gateway; legacy views remain as fallback.
- Migrate order and returns tables via CDC; reconcile daily during the 60-day dual-run window.
- Validate that the returns process (including cross-border returns across the 8 countries) works identically.
- Rollback re-routes order queries to the monolith; event replay ensures no order is lost.
    17. Extract Back-Office and Admin Portal (Wave 7) (depends on: 16)
       Deliver a **modern back-office** for the 300 staff users, consuming the new service APIs.

- Build a new back-office frontend (React or Vue SPA) backed by a thin BFF (Backend-for-Frontend) that aggregates calls to catalogue, pricing, order, inventory, and customer services.
- Migrate back-office routes incrementally via the gateway; legacy server-rendered admin pages remain accessible.
- Implement role-based access control (RBAC) and audit logging as cross-cutting concerns in the BFF.
- Run parallel operation for 4 weeks: staff use the new portal with a feedback channel; legacy portal stays one click away.
- Decommission legacy admin screens only after 30 days of zero critical issues.
- Provide training sessions and documentation for all 300 back-office users.
    18. Storefront Modernisation and Mobile-App API Alignment (depends on: 11, 14, 15)
       Update the **customer-facing storefront and mobile-app integration** to consume the new service layer.

- Refactor the server-rendered storefront to call service APIs via the gateway instead of hitting monolith endpoints directly.
- Introduce a Storefront BFF that aggregates catalogue, pricing, cart, and customer data for page rendering.
- Ensure the mobile app switches to the new API version behind the gateway; enforce backward compatibility for two app-release cycles.
- Implement edge caching (CDN + Varnish) for catalogue and search responses to protect services during 12× peaks.
- Validate all 4 language / 3 currency combinations through automated E2E tests.
- Rollback: gateway routes storefront traffic back to the monolith rendering path.
    19. Peak-Season Load Testing and Resilience Validation (depends on: 9, 15, 16)
       Prove the platform sustains **12× peak load** before the January and July sales windows.

- Build a load-test suite (Gatling or k6) replicating the full user journey across all 8 countries, including promo-code-heavy scenarios.
- Execute a full 12× load test in a staging environment that mirrors production topology, data volume (1.2 TB replica), and service versions.
- Run chaos-engineering experiments: kill random pods, introduce network latency, take a payment provider offline, simulate Kafka broker loss.
- Validate autoscaling: confirm that pod counts scale to handle peak within 90 seconds and scale down gracefully.
- Produce a signed-off capacity report; any component that fails the 12× test blocks go-live.
- Schedule this step at least 3 weeks before each peak season (early December for January sales, early June for July sales).
    20. Monolith Decommission and Final Data Migration (depends on: 16, 17, 18, 19)
       Retire the legacy monolith **only after all traffic is served by the new services**.

- Verify that zero production requests route to the monolith for 30 consecutive days.
- Perform a final data reconciliation: compare monolith DB checksums against service-owned databases.
- Archive the monolith codebase and database; retain read-only access for 12 months for audit and compliance.
- Decommission monolith infrastructure; reclaim compute and storage resources.
- Update all runbooks, on-call rotations, and disaster-recovery plans to reference the new service topology.
- Conduct a cross-team retrospective documenting lessons learned, technical debt incurred, and future improvement backlog.

Please, considering the previous proposals as ideas that could be considered, focus on the main objective and generate an IMPROVED proposal or a completely DIFFERENT perspective if you deem it appropriate. Only if you consider any of them is amazing and impossible to improve, answer with the same proposal.

The plan has these parts:
- "steps": the list of steps, each with "step_id" (a short label unique within this proposal, such as S1, S2, S3), "title" (a short, descriptive title), "description" (what the step does and how, in Markdown: a short opening paragraph and then, if there are several concrete things to say, a bullet list) and "dependencies" (the step_ids of the other steps of THIS proposal that must be completed first; empty if none).
- "estimated_complexity": "low", "medium" or "high".
- "success_metrics": clear and measurable success metrics, one per line as a bullet list.

Think deeply about the problem, considering all aspects and implications. Develop a comprehensive understanding of what needs to be done to achieve the objective, then produce the plan.

{"steps": [{"step_id": "S1", "title": "Launch the migration programme and protect revenue", "description": "Create a delivery model that treats peak trading, financial correctness, and reversibility as non-negotiable constraints.\n\n- Appoint an accountable programme lead, chief architect, domain owners, operations lead, security/privacy lead, and business owners for pricing, finance, warehouse, and country operations.\n- Reserve team capacity: 50% roadmap delivery, 30% migration, and 20% quality, operational resilience, and unplanned work. Reprioritisation requires steering approval.\n- Publish decision rights, architecture principles, risk register, dependency board, escalation process, and a weekly engineering-business steering cadence.\n- Define sales-protection windows: no first production cutover, ownership transfer, destructive schema change, payment change, or traffic increase in the six weeks before, during, and two weeks after each January and July sale period.\n- Feature work continues throughout. New capabilities use flags and compatible interfaces so deployment is separated from customer release.", "dependencies": []}, {"step_id": "S2", "title": "Establish the factual baseline and critical invariants", "description": "Measure current behaviour before changing it. The baseline is the comparison point for every migration decision and rollback.\n\n- Trace storefront, mobile, back-office, warehouse, payment, scheduled-job, and support journeys through code, endpoints, tables, stored procedures, and external integrations.\n- Inventory all 350 tables, stored procedures, triggers, files, writers, readers, cross-module joins, data classifications, retention rules, and GDPR obligations.\n- Record normal and sale-peak traffic by country, language, currency, page type, payment method, and warehouse flow. Capture p50/p95/p99 latency, errors, conversion, approval rate, database saturation, and recovery time.\n- Define non-negotiable business invariants: price and tax correctness, promotion eligibility, no duplicate payment or order, stock-reservation semantics, refund integrity, loyalty ledger integrity, and warehouse export completeness.\n- Produce an extraction scorecard using coupling, change rate, business risk, data ownership feasibility, rollback quality, and value.", "dependencies": ["S1"]}, {"step_id": "S3", "title": "Set target boundaries and realistic 12-month scope", "description": "Define bounded contexts and data ownership without committing to a risky monolith retirement date. The target is independently deployable capabilities, not a big-bang rewrite.\n\n- Define initial domains: edge/storefront, catalogue, search, pricing and promotions, customer and loyalty, inventory, cart, checkout, payment adapters, orders, returns, and back-office workflows.\n- Assign one accountable owner and one system of record for every entity group. A service may hold a replicated read model but may never write another service's database.\n- Set transition states: monolith-owned, replicated read model, shadow-validated, service command owner with legacy adapter, and legacy-retired.\n- Prohibit distributed transactions and uncontrolled dual writes. Use one command owner, transactional outbox, idempotency, compensations, reconciliation, and business exception queues.\n- Set the year-one exit scope: independently deployable edge, search, catalogue reads, inventory integration and availability reads, customer/profile slices, order-query and returns slices, payment adapters, pricing façade and proven rule slices, plus a checkout façade. Transfer transactional ownership only where evidence gates pass.\n- Keep the legacy pricing engine and core order creation available behind compatible façades if full ownership transfer is not proven safe by month 12.", "dependencies": ["S2"]}, {"step_id": "S4", "title": "Create the peak calendar and release-control policy", "description": "Turn the January and July constraint into an executable calendar and change policy.\n\n- Map the 12 months against the actual sale dates, country-specific campaigns, warehouse stocktakes, payment-provider freezes, and mobile release schedules.\n- Schedule capacity rehearsals at least six weeks before each peak and freeze traffic expansion before the protection window begins.\n- Define permitted work in protection windows: monitoring, capacity changes, reversible defect fixes, rehearsed rollback exercises, and business features already proven behind dormant flags.\n- Require a formal go/no-go review for every material migration, with operations holding veto authority for checkout, payment, search, and inventory changes.\n- Maintain a change ledger showing route, flag, schema version, source of truth, rollback action, responsible on-call team, and customer impact.", "dependencies": ["S1", "S2"]}, {"step_id": "S5", "title": "Instrument the monolith and define operational objectives", "description": "Make the existing estate observable before any production traffic is moved.\n\n- Add correlation IDs, structured logs, metrics, traces, business events, synthetic transactions, and real-user monitoring to the monolith and its external boundaries.\n- Define SLOs and error budgets for browse, search, product page, price quote, cart, checkout, payment, order confirmation, inventory freshness, back-office, and warehouse exchange.\n- Alert on customer and financial outcomes, including price mismatches, payment/order mismatch, inventory discrepancies, event lag, search zero-result changes, and failed warehouse files.\n- Build side-by-side dashboards for legacy and replacement paths. Include country, currency, language, payment provider, and traffic cohort dimensions.\n- Test backup, restore, database failover, provider outage handling, and incident communications before service traffic is introduced.", "dependencies": ["S2", "S3"]}, {"step_id": "S6", "title": "Build the paved road for independently deployable services", "description": "Deliver a small, standard platform that lowers operational risk rather than introducing unnecessary infrastructure complexity.\n\n- Provide templates for Java services with health and readiness checks, graceful shutdown, OpenTelemetry, authentication, configuration, secrets, database migrations, API documentation, outbox publishing, and idempotent consumers.\n- Create CI/CD pipelines with provenance, dependency and container scanning, unit, integration, contract, smoke, performance, and deployment checks.\n- Provision isolated integration, staging, performance, and production environments through infrastructure as code. Use managed or highly available runtime, database, cache, and messaging services appropriate to the retailer's operating model.\n- Implement progressive delivery with flags, canary or blue/green deployment, automated SLO-based rollback, deployment freeze controls, and auditable approvals for financial changes.\n- Establish least-privilege service identities, secret rotation, encryption, vulnerability management, audit logging, PCI scope assessment, and GDPR controls.", "dependencies": ["S3", "S5"]}, {"step_id": "S7", "title": "Stabilise and modularise the live monolith", "description": "Make the monolith safer to coexist with services while preserving feature delivery.\n\n- Establish code ownership and architecture tests for domain package boundaries. Prevent new cross-domain table access, joins, and stored-procedure dependencies.\n- Introduce branch-by-abstraction interfaces around candidate domains, beginning with search, catalogue, pricing, inventory, customer, and payment-provider logic.\n- Apply expand-contract rules for all schema changes. Additive changes precede code changes; destructive changes require a consumer inventory and completed observation period.\n- Add kill switches to every new monolith-to-service integration. Prove online deployment, connection draining, and backward-compatible schema releases to reduce reliance on the 30-minute maintenance window.\n- Capture characterization tests around high-risk stored procedures and APIs before modifying or replacing them.", "dependencies": ["S2", "S5", "S6"]}, {"step_id": "S8", "title": "Implement governed events, replication, and reconciliation", "description": "Build reusable coexistence patterns before moving any data or command responsibility.\n\n- Deploy an event backbone with schema governance, compatibility checks, retention, replay, dead-letter handling, consumer ownership, and throughput sized beyond the 12x sale profile.\n- Add transactional outbox publishing to new writes and selected monolith modules. Use CDC only where an outbox cannot yet be introduced, with a documented retirement plan.\n- Build a replication framework for initial backfill, checkpoints, replay, lag monitoring, checksums, record-level comparisons, financial totals, stock totals, and exception workflows.\n- Standardise anti-corruption adapters and versioned API/event contracts. Include timeouts, retries, bulkheads, circuit breakers, correlation IDs, and idempotency keys.\n- Define the rollback rule: route writes to one compatible command owner. A route rollback must preserve writes already accepted by the new path through events or compatibility adapters; it must never discard or blindly reverse financial records.", "dependencies": ["S3", "S6", "S7"]}, {"step_id": "S9", "title": "Build risk-weighted quality and capacity assurance", "description": "Replace confidence based on a fortnightly release with automated evidence for customer and financial journeys.\n\n- Create anonymised, production-shaped fixtures covering eight countries, three currencies, four languages, tax, promotions, guest and registered customers, warehouse states, and all payment-provider outcomes.\n- Automate characterization, API, contract, integration, end-to-end, data-reconciliation, load, soak, spike, failover, and chaos tests. Prioritise affected paths over a blanket line-coverage target.\n- Require 100% automated coverage of defined price, payment, order, refund, stock-reservation, and loyalty-ledger scenarios before their ownership can change.\n- Establish a production-like performance environment and provider and warehouse simulators. Test the hybrid path, not services in isolation.\n- Make release gates explicit: observability, rollback rehearsal, compatible contracts, reconciliation, security, and capacity evidence are required before traffic expansion.", "dependencies": ["S2", "S5", "S6", "S8"]}, {"step_id": "S10", "title": "Introduce edge routing and stable channel façades", "description": "Decouple web, mobile, and back-office clients from monolith implementation paths while keeping their current contracts intact.\n\n- Place an API gateway and, where needed, backend-for-frontend façade in front of existing endpoints without changing initial functional behaviour.\n- Route by path, country, cohort, flag, and percentage. Default all routes to the monolith until promotion criteria are met.\n- Preserve mobile API compatibility, cookies or tokens, sessions, headers, localization, and server-rendered storefront behaviour. Do not require a mobile-app release for a backend migration.\n- Add traffic mirroring only for safe, read-only requests or explicitly idempotent shadow calls. Never duplicate customer-visible commands or payment requests.\n- Test instant route rollback, cache bypass, session continuity, in-flight request draining, and full-load reversion to the monolith.", "dependencies": ["S5", "S6", "S7", "S9"]}, {"step_id": "S11", "title": "Extract catalogue reads and modernise search", "description": "Use read-heavy, reversible customer-facing capabilities as the first full production migration pattern.\n\n- Build a catalogue read service fed from monolith-owned data through controlled replication and events. Keep content and product command ownership in the monolith initially.\n- Build an independently operated search service with incremental indexing, aliases, blue/green indexes, locale-aware analysis, cache controls, and rapid fallback to the existing Lucene index.\n- Shadow-compare product content, availability display, localization, ranking, facets, price display version, zero-result rate, latency, and conversion against the legacy path.\n- Progress through employee traffic, low-risk cohorts, country-by-country rollout, and percentage expansion. Maintain the legacy route and warm index through at least one peak period after full traffic migration.\n- Do not make search authoritative for stock or price. It consumes explicitly versioned read models from their command owners.", "dependencies": ["S4", "S8", "S9", "S10"]}, {"step_id": "S12", "title": "Modernise warehouse integration and inventory availability reads", "description": "Separate warehouse file handling and customer availability reads without prematurely moving stock reservation ownership.\n\n- Build a warehouse adapter that validates, journals, deduplicates, acknowledges, and replays current inbound and outbound file exchanges without requiring warehouse-side change.\n- Publish inventory changes and build an availability read model with explicit freshness, safety-stock, fulfilment-node, country, and oversell semantics.\n- Shadow-compare every availability result against the monolith. Reconcile per SKU, warehouse, and order state, and route operational exceptions to trained teams.\n- Move storefront and search availability reads progressively. Retain monolith reservation, allocation, and warehouse-export authority until checkout transition design is proven.\n- Test delayed files, duplicate files, malformed files, replay, inventory-event lag, and fallback to monolith reads under peak load.", "dependencies": ["S4", "S8", "S9", "S10"]}, {"step_id": "S13", "title": "Contain pricing and promotions through archaeology and a façade", "description": "Treat pricing as a behaviour-preservation programme before it becomes a service extraction programme.\n\n- Form a dedicated squad with senior engineers, merchandising, finance, country representatives, customer support, and QA.\n- Inventory code, stored procedures, configuration tables, overrides, scheduled jobs, manual back-office actions, and external inputs for all price, tax, discount, voucher, and promotion decisions.\n- Capture privacy-safe production decision traces and build a golden-master corpus across countries, currencies, dates, customer segments, baskets, stacking, tax, inventory conditions, and edge cases.\n- Put the existing engine behind a versioned pricing façade. New callers use this façade even while it delegates to legacy logic.\n- Classify rules into independently movable slices. Build a candidate evaluator only for understood slices, shadow-compare exact amount, currency, tax, explanation, eligibility, and latency, and require business sign-off for every accepted difference.", "dependencies": ["S2", "S7", "S8", "S9", "S10"]}, {"step_id": "S14", "title": "Extract customer, consent, and bounded loyalty capabilities", "description": "Move identity-adjacent capabilities in carefully bounded slices, starting with reads and avoiding inconsistent account state.\n\n- Define canonical customer identity, authentication/session compatibility, consent, retention, subject access, deletion, address, and access-control rules.\n- Start with replicated profile and loyalty-balance reads. Move profile writes through one idempotent service command path only after daily reconciliation is clean.\n- Represent loyalty accrual and redemption as an auditable ledger. Migrate balance inquiry before financial-impacting redemption or accrual.\n- Retain compatibility adapters for monolith and legacy back-office functions. Support web and mobile clients without forced logout or password reset.\n- Reconcile customer records, consent, addresses, and loyalty balances daily. Keep a staffed exception process and explicit data-subject request procedures during transition.", "dependencies": ["S8", "S9", "S10"]}, {"step_id": "S15", "title": "Extract order views and bounded post-order workflows", "description": "Create order-domain value without splitting the revenue-critical order-creation transaction too early.\n\n- Publish reliable order lifecycle events from the current command owner through the outbox pattern.\n- Build an order query service for customer self-service, support, notifications, and selected back-office reads. Display freshness and preserve a legacy support fallback.\n- Extract bounded workflows such as return initiation, return tracking, notification delivery, and non-financial enrichment where the ownership boundary is clear.\n- Reconcile order counts, state transitions, delivery notifications, returns, refunds, event lag, and customer-service views against the monolith.\n- Keep order creation, cancellation, payment capture coordination, financial refund authority, and warehouse order export under the current owner until checkout cutover gates are passed.", "dependencies": ["S8", "S9", "S10", "S12", "S14"]}, {"step_id": "S16", "title": "Introduce payment-provider adapters and financial reconciliation", "description": "Isolate provider-specific complexity before changing checkout orchestration or payment ownership.\n\n- Wrap each payment provider behind a versioned adapter with token handling, webhook verification, idempotent authorisation and capture, timeout policy, provider-specific retries, and controlled fallback behaviour.\n- Introduce a payment ledger and daily reconciliation across authorisations, captures, refunds, chargebacks, settlements, and order states.\n- Validate adapter behaviour with provider sandboxes, recorded non-sensitive production outcomes, failure injection, and controlled internal cohorts. Do not mirror live payment commands.\n- Preserve existing customer-facing errors and country/payment-method routing during initial adoption.\n- Make rollback safe for in-flight operations: accepted payment attempts retain the same idempotency key and completion path, while new attempts route back through the compatible legacy path.", "dependencies": ["S8", "S9", "S10", "S15"]}, {"step_id": "S17", "title": "Move proven pricing slices and prepare cart and checkout façades", "description": "Use pricing parity evidence to move only safe rule slices, then establish compatible façades for cart and checkout.\n\n- Run the candidate pricing service in shadow for all applicable quotes. Investigate every mismatch and quantify financial impact before any live traffic.\n- Migrate rules by bounded slice, country, and promotion type. Keep a per-slice route-back switch to the legacy engine and retain legacy execution through at least the next relevant sale period.\n- Define cart identity, guest-to-account merge, session persistence, currency and country changes, promotion snapshots, inventory-check semantics, and cart expiry rules.\n- Introduce cart and checkout façades that initially delegate to legacy commands. This creates a stable integration seam without changing transaction authority.\n- Add checkout idempotency keys, a durable attempt state machine, explicit compensation paths, and customer-support procedures for ambiguous payment, stock, and order outcomes.", "dependencies": ["S11", "S12", "S13", "S14", "S15", "S16"]}, {"step_id": "S18", "title": "Progressively migrate cart and checkout orchestration", "description": "Transfer only the proven portions of the transactional path, country and payment method by country and payment method, with the legacy path retained as a compatible recovery route.\n\n- Start with cart reads and writes, using one command owner at each stage and reconciliation of active, abandoned, merged, and promotional carts.\n- Move checkout orchestration only after end-to-end failure-mode analysis proves correct handling of payment timeout, duplicate callback, stock loss, order-write failure, and customer retry.\n- Canary by internal cohort, low-risk country, payment method, and percentage. Expand only when conversion, payment approval, order completion, price parity, stock discrepancy, latency, and support-contact thresholds are met.\n- Use a durable orchestration state and outbox events rather than a distributed database transaction. Compensate or route exceptions; do not silently retry customer financial commands.\n- If ownership transfer is not safe before a protected sales window, retain the independently deployable façade delegating to the monolith. This still permits independent release of channel and resilience improvements without risking orders.", "dependencies": ["S4", "S9", "S12", "S16", "S17"]}, {"step_id": "S19", "title": "Transfer data ownership one entity group at a time", "description": "Perform write cutovers as controlled state transitions, not as a one-time database split.\n\n- For each entity group, document source of truth, writers, readers, stored procedures, consumers, migration checkpoint, backfill method, replication direction, retention requirements, reconciliation thresholds, and rollback mechanics.\n- Backfill with checksums and resumable batches. Validate dual reads before changing a command route, then transfer one writer path through a compatible API or adapter.\n- Stop traffic expansion automatically if reconciliation thresholds are breached. Financial discrepancies require immediate investigation and no unresolved discrepancy is accepted.\n- Retain legacy read access, compatibility APIs, and replay capability for an agreed observation period. Do not delete data, tables, procedures, or flags as part of initial ownership transfer.\n- Begin with low-risk read-model ownership. Transfer inventory reservation, loyalty redemption, pricing command rules, and core order ownership only after their specific evidence gates and outside sales windows.", "dependencies": ["S8", "S11", "S12", "S14", "S15", "S17", "S18"]}, {"step_id": "S20", "title": "Migrate back-office workflows incrementally", "description": "Move the 300 staff users by workflow and role, not through a high-risk replacement of the entire administration application.\n\n- Deliver domain-specific back-office screens or BFF capabilities that use the same governed APIs and audit controls as customer-facing channels.\n- Start with read-only catalogue, order-query, return-status, and inventory views. Move commands only after service ownership and approval controls are established.\n- Preserve role-based access, segregation of duties, audit logs, country entitlements, exports, and operational exception handling.\n- Run old and new screens in parallel for each workflow. Provide training, floor support, feedback capture, and a direct fallback during the adoption period.\n- Remove direct SQL access to migrated data and replace necessary reports with governed read models or reporting exports.", "dependencies": ["S11", "S12", "S14", "S15", "S19"]}, {"step_id": "S21", "title": "Certify hybrid peak readiness and rehearse reversions", "description": "Certify the actual mixed estate before each January and July peak. Every fallback must handle the traffic it may receive after a rollback.\n\n- Load, soak, spike, and failover test at least 12x observed normal demand plus agreed headroom across gateway, CDN/cache, monolith, databases, services, search, event platform, warehouse adapter, and payment adapters.\n- Test reversion of each live route to the monolith or compatible predecessor at full expected load. Confirm capacity reservations, cache warming, database connection limits, autoscaling limits, and provider rate limits.\n- Run game days for service loss, database failover, event delay or duplication, cache failure, search fallback, warehouse-file delay, payment-provider outage, and flag or route rollback.\n- Conduct incident-command and customer-support rehearsals. Verify runbooks, contacts, communications, dashboards, and business exception queues.\n- Require written sign-off from engineering, operations, commerce, finance, warehouse, payments, and customer support before entering each protection window.", "dependencies": ["S4", "S5", "S9", "S11", "S12", "S16", "S18"]}, {"step_id": "S22", "title": "Consolidate proven services and hand over sustainable operations", "description": "Complete the year by removing only obsolete paths and establishing durable ownership. Retain legacy components where their removal would weaken safety.\n\n- Confirm each independently deployable service has a named team, on-call coverage, SLOs, dashboards, runbooks, capacity model, disaster-recovery procedure, and tested rollback or recovery route.\n- Retire temporary replication, legacy endpoints, stored procedures, batch jobs, tables, and flags only after all consumers have moved, reconciliations are clean, and the agreed rollback-retention period has passed.\n- Measure residual monolith coupling, direct database access, synchronous dependency depth, event lag, deployment frequency, incident recovery, and operational toil.\n- Archive required legacy data for audit and GDPR obligations. Keep a documented compatibility and read-only access strategy where business or regulatory retention requires it.\n- Publish the funded follow-on roadmap for any core pricing, checkout, or order ownership that correctly remained in the monolith because its safety gates were not met within the year.", "dependencies": ["S19", "S20", "S21"]}], "estimated_complexity": "high", "success_metrics": "- No unplanned customer-facing downtime attributable to migration work during the 12-month programme.\n- Every production migration has a documented and rehearsed rollback or recovery action; routing rollback completes within 5 minutes and migration-related severity-one recovery completes within 30 minutes.\n- January and July sales achieve at least the pre-programme availability, conversion rate, payment approval rate, order throughput, and inventory accuracy at 12x observed normal load plus agreed headroom.\n- No first-time cutover, ownership transfer, destructive schema change, or traffic expansion occurs inside the defined sales-protection windows.\n- Critical journeys have 100% automated coverage of defined price, payment, order, refund, stock reservation, and loyalty-ledger scenarios; all changed migration paths have contract, integration, and reconciliation tests.\n- Search, catalogue reads, inventory availability and warehouse adapter, customer/profile slices, order-query and returns slices, payment adapters, and pricing façade are independently deployable with named ownership and operational readiness by month 12.\n- Cart and checkout are independently deployable façades by month 12; transactional command ownership transfers only where stated parity, reconciliation, failure-mode, and peak-capacity gates pass.\n- Pricing rule slices receive live traffic only after at least 99.99% exact parity on approved golden-master and production-shadow cases, with every accepted difference approved by business and finance.\n- Every extracted service has zero direct writes to another service database; cross-service state propagation uses versioned APIs or events with idempotency and monitored replay.\n- For each ownership cutover, unresolved record discrepancies remain below 0.01%, with zero unresolved payment, refund, tax, loyalty-ledger, or order-total discrepancies.\n- The hybrid platform passes full-path load and reversion testing at 12x normal demand plus headroom before each sales period.\n- Routine compatible service releases can be deployed at least weekly without the monolith maintenance window, while roadmap delivery remains at least 80% of the agreed pre-programme baseline."}

[SYSTEM]
You are an expert assistant in complex project planning.
Your task is to generate a detailed and structured action plan to reach the main objective in the most professional and most detailed way, no matter how much work or steps will be needed to perform.
Use your internal reasoning processes to think deeply about the problem and create the most comprehensive plan possible.
Take as much time and space as you need to think through all aspects of the problem.
After your thorough analysis, answer with the plan in the requested structure.

Every text field you write will be read by a busy person, so write for them: short sentences, one idea each, in short paragraphs. Text fields accept Markdown: separate paragraphs with a blank line, use a bullet list when you enumerate things and plain paragraphs when you explain or argue, and bold at most one key phrase per paragraph. A text of more than three sentences must be split into paragraphs; never deliver a single unbroken block of text.

[HUMAN]
Main Objective: "A mid-size European fashion retailer runs its e-commerce on a 10-year-old monolith: Java 8 / Spring, about 2 million lines, one PostgreSQL database of 1.2 TB with 350 tables and heavy use of stored procedures and cross-module joins. It serves 8 countries, 3 currencies and 4 languages, with about 40,000 orders a day and peaks of 12x during sales. The monolith contains: the storefront (server-rendered, plus a separate mobile app hitting the same endpoints), catalogue and search (a Lucene index rebuilt nightly), pricing and promotions (the most complex module, 200,000 lines, with country-specific rules nobody fully understands), cart and checkout with three payment providers, order management, inventory synchronised every 15 minutes with the warehouse system via file exchange, customer accounts and loyalty, returns, and a back-office used by 300 staff. Deployments happen every two weeks as a single artefact with a 30-minute maintenance window; test coverage is 25% and mostly unit tests. Five teams of 8 developers, each organised around a business area but all committing to the same repository.

Plan the migration to independently deployable services over 12 months with no feature freeze, no unplanned downtime and the ability to roll back every step, preserving the peak-season capacity (sales in January and July must not be put at risk)."


For your consideration and refinement, here are proposals from the previous round:

  Previous Proposal 1 (ID: 655fdafb-56dd-4ad6-bd0f-4bb657813959, Agent: claudeHaiku4.5_initial_1, LLM: anthropic/claude-haiku-4-5):
    Estimated Complexity: high
    Success Metrics: - All major business functions (catalog, pricing, orders, payments, inventory) deployed as independently releasable services by end of month 12.
- Deployment frequency increased from bi-weekly to daily or on-demand per team, with zero coordination required between teams.
- Peak-season capacity maintained: handle 40,000 orders/day baseline and 480,000 orders/day (12x peak) with P95 page load time ≤2 seconds and payment success rate >99.9%.
- Zero unplanned downtime caused by the migration during the 12-month period; any incidents are handled with automated rollback within 5 minutes.
- Test coverage increased from 25% to ≥70% across all services, with comprehensive integration test suite running on every commit.
- Mean time to recovery (MTTR) for production incidents reduced from hours to <15 minutes due to circuit breakers and fallbacks.
- Data consistency validated: automatic nightly checks confirm service data matches source-of-truth, with any discrepancies logged and investigated within 24 hours.
- Service API latency (p95) meets SLOs: catalog ≤200ms, pricing ≤300ms, inventory ≤200ms, payment ≤1000ms, with circuit breakers preventing cascading failures.
- Each service has documented runbooks, incident response procedures, and scaling playbooks; all ops and dev teams trained and confident.
- Feature delivery velocity maintained at pre-migration levels: new feature deployment time remains unchanged despite architectural changes.
    Steps (23):
    1. Establish governance and migration steering committee
       Create a governance structure to guide the 12-month migration and ensure alignment across teams.

- Define clear decision-making authority and escalation paths.
- Establish weekly steering meetings with representatives from each of the five teams plus leadership.
- Create a shared vision for service boundaries and prioritize which modules to extract first.
- Set up RACI matrix (responsible, accountable, consulted, informed) for each major service extraction.
    2. Design service architecture and system boundaries (depends on: 1)
       Map the monolith into independently deployable services with clear boundaries and synchronization points.

- Analyze the 350 tables and identify which tables belong to each business domain (catalog, pricing, orders, inventory, etc.).
- Design the data synchronization strategy for the 1.2 TB database, including which data moves to which service.
- Plan the strangler approach for each module: what gets extracted first, what depends on what.
- Define API contracts and asynchronous messaging patterns (events vs. direct calls) between services.
    3. Deploy Kubernetes infrastructure and container registry (depends on: 2)
       Build the cloud infrastructure to run containerized services at scale with redundancy and monitoring.

- Provision a production-grade Kubernetes cluster (managed service like EKS, AKS, or on-premises).
- Set up container image registry with retention policies and security scanning.
- Configure persistent storage volumes for databases and caches.
- Implement cluster networking, RBAC, and network policies for security.
    4. Implement strangler proxy and API gateway (depends on: 3)
       Deploy a reverse proxy that routes requests between the monolith and the new services, enabling gradual traffic migration.

- Deploy API gateway (e.g., Kong, Ambassador, or cloud-native option) in front of the monolith.
- Implement request routing logic to direct traffic to services or monolith based on rules.
- Add request/response logging and transformation layer for API versioning.
- Enable instant traffic rerouting and rollback if a service fails.
    5. Build feature flagging and traffic routing system (depends on: 3)
       Implement a system to control which users hit which service, enabling safe canary deployments and A/B testing.

- Choose or build a feature flag platform (LaunchDarkly, Unleash, or open source).
- Integrate with API gateway and service code to support percentage-based traffic splitting.
- Enable per-user, per-region, and per-browser routing for testing (e.g., mobile app to new service before web).
- Create dashboards for ops teams to adjust routing in real time without redeploying.
    6. Establish comprehensive observability (logs, metrics, tracing) (depends on: 3)
       Deploy centralized logging, metrics, and tracing to track behavior across services and detect issues early.

- Set up centralized log aggregation (e.g., ELK, Splunk, or cloud-native solution).
- Deploy metrics collection (Prometheus, Datadog, or equivalent) with dashboards for each service.
- Implement distributed tracing (Jaeger, Zipkin) to track requests across service boundaries.
- Define critical alerts: error rates, latency spikes, database query performance, payment transaction failures.
    7. Design event-driven data consistency architecture (depends on: 3)
       Plan how services will stay in sync when sharing data extracted from the monolith's single database.

- Design an event bus or message queue topology (Kafka, RabbitMQ, or cloud equivalent).
- Plan Change Data Capture (CDC) from the monolith to notify services when data changes.
- Define saga patterns for multi-step distributed transactions (e.g., order creation spanning multiple services).
- Document how to handle eventual consistency, conflicts, and zombie data in each service.
    8. Build inter-service communication framework (APIs and queues) (depends on: 3)
       Establish libraries and standards for how services talk to each other synchronously and asynchronously.

- Define REST or gRPC standards (authentication, versioning, error handling) for all service-to-service calls.
- Create shared libraries for message publishing/consuming (idempotency, dead-letter handling).
- Document timeout and retry policies to prevent cascading failures.
- Provide templates and SDKs to development teams so they don't reimplement these patterns.
    9. Extract catalog and search service (depends on: 4, 5, 6, 8)
       Extract the catalog and Lucene search index into its own service, starting with a low-risk module to validate the pattern.

- Move catalog module code from monolith to a new service repository.
- Containerize the service and deploy to Kubernetes.
- Keep the existing Lucene index and nightly rebuild process initially.
- Route catalog API requests through the gateway: send 10% of traffic to new service first, validate results, increase to 100%.
    10. Create independent catalog data layer with synchronization (depends on: 9, 7)
       Extract catalog tables from the shared database and sync changes from the monolith to the new service.

- Copy catalog tables to a new PostgreSQL database managed by the catalog service.
- Implement CDC (Change Data Capture) to publish catalog changes as events when the monolith updates data.
- Build catalog service to subscribe to these events and update its own tables.
- Implement consistency checks: run hourly validation that catalog service data matches monolith source-of-truth, log discrepancies.
    11. Extract customer accounts service (depends on: 4, 5, 6, 8)
       Move customer profile, login, and loyalty data into a dedicated service that other services query.

- Extract customer and loyalty tables from monolith database.
- Build service to manage customer profile, authentication, and loyalty points.
- Implement event stream for customer changes (profile updates, loyalty point transactions).
- Route customer API calls through gateway; monolith and new service share database briefly, then switch to CDC sync.
    12. Extract returns management service (depends on: 4, 5, 6, 8)
       Create a focused returns processing service to further validate the extraction pattern and learn before tackling complex modules.

- Move returns processing logic and tables from monolith.
- Build simple service with clear inputs (return requests) and outputs (refund events).
- Connect to order data via API calls (will be extracted separately) and inventory service.
- Canary traffic, monitor error rates and latency; this is the lowest-risk extraction.
    13. Audit, document, and decompose pricing/promotions business rules (depends on: 1)
       Reverse-engineer and document the complex pricing logic to enable rebuilding it as a new service. Start early in parallel with infrastructure work.

- Form a task force: architects, the original pricing team, and business analysts.
- Read through the 200k lines of pricing code; document country-specific rules, exceptions, and dependencies (which rules call which).
- Build a comprehensive spreadsheet of pricing scenarios: free shipping rules, discount types, country-specific taxes, dynamic pricing, etc.
- Extract test cases from production data: get 1,000 real orders from each country and document how pricing rules applied.
- Identify which pricing decisions depend on cart, inventory, or customer account data.
    14. Design and implement pricing/promotions service with enhanced testing (depends on: 4, 5, 6, 8, 13)
       Rebuild the pricing logic as a new microservice with a cleaner architecture and comprehensive test coverage.

- Architect the new service with clear separation: promotion evaluation, tax calculation, discount application, price transformation per country.
- Implement each country's rules as either code or a rules engine (not hardcoded strings).
- Build unit tests for 100+ pricing scenarios (cross-reference with S13 test cases).
- Implement shadow traffic testing: send real production requests to both monolith and new service, log differences, investigate discrepancies before switching traffic.
    15. Implement event-driven pricing and cart synchronization (depends on: 14, 7, 9)
       Sync pricing changes and promotions between the pricing service and cart/checkout to keep pricing consistent in real time.

- Publish events when promotions are created/updated: promotion_created, promotion_updated, promotion_ended.
- Implement cart service subscription: when a cart is modified or promotion changes, recalculate cart total.
- Handle time-based promotions: if a promotion starts/ends during a customer's shopping, reflect immediately.
- Validate consistency: sample 1% of checkouts, compare price calculated by pricing service vs. what customer paid; alert if mismatch.
    16. Extract inventory management service (depends on: 4, 5, 6, 8, 10)
       Create a service that manages stock levels and warehouse synchronization, replacing the 15-minute batch sync with event-driven updates.

- Extract inventory tables and warehouse sync logic from monolith.
- Build inventory service that subscribes to warehouse file drops (replace file exchange with event publishing or direct API).
- Implement real-time inventory updates: when an order is placed, reserve stock immediately; when warehouse sends stock count, update available qty.
- Canary deploy and validate: monitor for stock mismatch errors (overselling); maintain monolith as source-of-truth with service as secondary initially.
    17. Extract payment gateway coordination service (depends on: 4, 5, 6, 8)
       Abstract the three payment providers into a dedicated service so checkout doesn't depend on external API details.

- Move payment provider logic (Stripe, PayPal, local provider) from monolith checkout to new service.
- Implement payment orchestration: route to correct provider based on country/currency, handle failures, retry logic.
- Build payment event stream: payment_initiated, payment_authorized, payment_captured, payment_failed, payment_refunded.
- Test thoroughly: use sandbox accounts, simulate failure scenarios (provider timeout, decline, network error); ensure consistent error messages to checkout.
- Use gateway to route: send payments for test users/regions to new service first.
    18. Implement resilience patterns across services (circuit breakers, fallbacks, retries) (depends on: 9, 10, 11, 12)
       Make services robust to failures of dependent services; services should handle failures gracefully, not crash the whole system.

- Install circuit breaker library (Resilience4j, Hystrix equivalent) in each service.
- Define circuit breaker policies per dependency: if catalog service is slow, circuit opens after 50 failures or 5 seconds slow response, fails fast.
- Implement fallback strategies: if pricing service is down, use cached pricing; if inventory is down, temporarily increase order-to-fulfillment delay.
- Set timeouts on all cross-service calls (e.g., cart→pricing must return in 500ms) with bulkhead pattern to prevent resource exhaustion.
- Test: use chaos monkey or chaos toolkit to inject failures (kill pods, add latency) and verify fallbacks work.
    19. Build comprehensive integration test suite (depends on: 14, 16, 17)
       Create automated tests that exercise real customer journeys across multiple services to catch bugs before production.

- Build test data setup: create products, customers, promos, inventory in test environment.
- Write end-to-end test scenarios: browse catalog → add to cart → apply promo → checkout with payment → order created → inventory updated → returns processing.
- Implement performance tests: simulate 40,000 orders/day baseline load, 480,000 orders (12x peak) burst load; validate response times and error rates.
- Add chaos tests: run scenarios while services fail (pod restart, network partition, database slow) to validate resilience.
- Run tests on every service commit and nightly against staging environment; alert on test failure.
    20. Create independent service deployment pipelines (depends on: 4, 18)
       Set up automated deployment so each service can be released independently without coordinating with other teams every two weeks.

- For each service: build → run tests → build container image → push to registry → deploy to staging with canary (5% traffic initially).
- Implement automated rollback: if error rate on new service exceeds threshold for 5 minutes, automatically route traffic back to old version and alert.
- Add manual approval gates for production: team lead reviews test results, approves, release happens with 0 downtime (health checks, graceful shutdown).
- Documentation: each team has runbook for deploying their service, rolling back, handling incidents.
- Target: enable each team to deploy 1-2 times per day if needed.
    21. Conduct load testing and peak-season capacity planning (depends on: 19, 20)
       Validate that the new service architecture can handle peak loads (40k baseline, 480k at 12x peak) without degradation.

- Load test in staging: ramp up traffic gradually, measure latency, error rate, and resource usage (CPU, memory, database connections).
- Identify bottlenecks: where does latency spike first? Is it database queries, service CPU, or network?
- For each service and the database: determine max capacity and burst capacity (e.g., catalog service handles 500 QPS sustained, 1000 QPS for 30s burst).
- Plan auto-scaling: set Kubernetes horizontal pod autoscaler min/max replicas, database read replicas, and caching layers based on results.
- Validate payment processing: simulate peak payment volume with all three providers; confirm no payments are lost or duplicated.
    22. Execute comprehensive pre-peak-season validation and simulation (depends on: 21)
       Run a full-dress rehearsal before January/July peak season to ensure the system is ready; critical gate before any further changes.

- Schedule 48-hour end-to-end test: run production-like load against all services with canary deployments to catch integration issues.
- Run disaster recovery drill: if one service is down, can customers still browse and checkout? If payment provider fails, can we use fallback?
- Customer journey validation: have real team members and friendly customers test: browse → add items → apply promo code → checkout on web and mobile apps.
- Team readiness: hold incident response drill, confirm runbooks are accurate, escalation paths clear, and alert thresholds are tuned.
- Performance sign-off: confirm P95 page load times, checkout flow time, order processing latency all meet SLOs for peak traffic.
- Go/no-go decision: leadership reviews results; if any critical issue, fix and re-test before peak season starts.
    23. Monitor, optimize, and prepare for ongoing evolution (depends on: 22)
       After validation, monitor the production system closely during peak season; optimize based on real behavior and plan next improvements.

- Daily monitoring during peak season: dashboard watching error rates, latency, payment success rate, customer support tickets.
- Real-time tuning: if one service is bottleneck, increase replicas or add caching; if database query is slow, add index (non-blocking).
- Post-peak analysis: compare actual peak performance to projections; document what assumptions were wrong, what worked well.
- Identify quick wins: which cross-service calls can be cached, which synchronous calls could be async, which services could be split further?
- Plan the next 6 months: which remaining monolith modules can be extracted, which services need optimization, should we change tech stack for any?

  Previous Proposal 2 (ID: 5f35320e-40bf-4797-a282-13ff94f785f1, Agent: gpt-5.6-terra_initial_2, LLM: openai/gpt-5.6-terra):
    Estimated Complexity: high
    Success Metrics: - No unplanned customer-facing downtime is attributable to migration work during the 12-month programme.
- Every production migration has a documented, rehearsed rollback that can be initiated within 15 minutes and preserves financial, order, payment, and inventory integrity.
- January and July sales periods complete with at least the pre-migration peak availability, conversion rate, payment approval rate, and order throughput.
- The hybrid platform sustains at least 12x observed normal load plus agreed headroom in full-path load and failover tests before each sales period.
- Critical journeys achieve at least 95% automated API, integration, contract, and end-to-end regression coverage by business-risk weighting, with 100% coverage of defined checkout, payment, order, stock, refund, and price-parity scenarios.
- Catalogue/search, inventory availability, customer/loyalty slices, order query/post-order slices, and selected checkout/payment façade capabilities are independently deployable with named ownership, SLOs, dashboards, runbooks, and on-call support.
- All extracted services have zero direct writes to another service's database, and all cross-service state propagation uses governed APIs or versioned events.
- For each migrated entity group, reconciliation identifies less than 0.01% unresolved record discrepancies and zero unresolved financial discrepancies at cutover completion.
- Pricing and promotion decision parity for any migrated rule slice is at least 99.99% against approved golden-master cases, with all remaining differences explicitly approved by business owners.
- Deployment frequency for independently deployable services reaches at least weekly, with no mandatory monolith maintenance window required for routine compatible releases.
- Mean time to detect critical customer-journey failures is below 5 minutes, and mean time to restore or roll back migration-related severity-one incidents is below 30 minutes.
- Feature delivery continues throughout the programme, with planned business roadmap throughput maintained at no less than 80% of the agreed baseline.
    Steps (20):
    1. Establish migration governance and delivery model
       Create a migration programme that protects revenue, peak periods, and ongoing feature delivery. Assign one accountable programme lead, a chief architect, and named business and operational owners for every domain.

- Create a steering group with engineering, product, operations, security, finance, warehouse, payments, and country representatives.
- Reserve capacity per team: 50% business delivery, 30% migration work, and 20% quality, operational, and unplanned-work reduction. Rebalance only through the steering group.
- Publish decision rights, architecture principles, risk register, dependency board, and weekly programme cadence.
- Define explicit stop/go criteria for each production cutover and a formal rollback authority.
- Plan sales protection windows: no first-time domain cutovers, database schema changes, payment changes, or major traffic experiments during the four weeks before and through January and July sales periods.
- Keep feature work flowing through the same delivery pipeline, with feature flags used to decouple code deployment from customer release.
    2. Baseline the monolith, traffic, data, and operational risk (depends on: 1)
       Build an evidence-based picture of the current system before selecting extraction order. The baseline becomes the capacity, correctness, and rollback reference for every migration wave.

- Map request flows from web, mobile, back-office, warehouse files, payment providers, and scheduled jobs to modules, tables, stored procedures, queues, and external dependencies.
- Measure normal and sale-peak throughput, latency, error rates, database load, index rebuild duration, batch duration, payment approval rates, and recovery times.
- Classify all 350 tables and stored procedures by owning business concept, writers, readers, sensitivity, retention requirements, and cross-module coupling.
- Identify critical business invariants, including stock reservation, price calculation, promotion eligibility, payment-to-order consistency, returns, loyalty accrual, and country tax requirements.
- Produce dependency heat maps and a candidate extraction scorecard using coupling, business risk, change frequency, data ownership feasibility, and expected value.
- Capture a production-like anonymised data set and documented peak-load profiles for repeatable testing.
    3. Define target architecture and domain boundaries (depends on: 2)
       Agree a pragmatic target architecture based on bounded contexts, clear data ownership, and incremental extraction. Do not start by redesigning every business process or splitting every table.

- Define initial bounded contexts: edge/storefront experience, catalogue, search, pricing and promotions, cart, checkout, payments, orders, inventory, customers and loyalty, returns, and back-office workflow.
- Assign a single system of record and an owning team for each business data entity. Services may consume replicated data but must not directly write another service's database.
- Define synchronous API rules, asynchronous event rules, versioning rules, idempotency requirements, correlation identifiers, and error-handling conventions.
- Establish a platform pattern: containerised services, managed or highly available PostgreSQL where appropriate, API gateway or edge routing, event transport, secrets management, central configuration, and infrastructure as code.
- Select an incremental strangler pattern. New services are introduced behind stable interfaces while the monolith remains the source of truth until ownership is deliberately transferred.
- Document explicitly that distributed transactions are prohibited. Use outbox, idempotent consumers, compensating actions, reconciliation, and business-visible exception queues instead.
    4. Create production safety foundations (depends on: 1, 3)
       Make every current and future component observable, operable, and auditable before material traffic is moved. This work starts in the monolith as well as in new services.

- Implement standard structured logs, metrics, distributed tracing, correlation IDs, service dashboards, synthetic customer journeys, and business KPIs.
- Define service-level objectives for storefront availability, search, price response, cart operations, checkout, payment confirmation, order creation, and warehouse export.
- Add alerting with severity, ownership, escalation paths, and tested runbooks. Alert on business failures as well as infrastructure failures.
- Establish immutable audit events for pricing changes, promotion decisions, payments, order state changes, stock adjustments, customer-data access, and administrative actions.
- Implement backup, restore, disaster recovery, and failover tests for the monolith database, new data stores, event platform, and search platform.
- Create a shared operations readiness review required before any service receives production traffic.
    5. Build secure delivery and runtime platform (depends on: 3, 4)
       Provide a paved road for independently deployable services. The platform must reduce deployment risk rather than create a second operational burden.

- Build standard service templates for Java, including health checks, readiness checks, graceful shutdown, telemetry, API documentation, authentication, configuration, database migrations, and outbox publishing.
- Implement CI/CD with build provenance, dependency and container scanning, automated unit, contract, integration, and smoke tests, environment promotion, and approval controls for high-risk releases.
- Provision isolated development, integration, staging, performance, and production environments through infrastructure as code.
- Introduce progressive delivery capabilities: feature flags, canary releases, blue/green deployment where justified, traffic splitting, automated rollback, and deployment freeze controls.
- Centralise secrets, certificate rotation, least-privilege service identities, encryption, vulnerability management, and GDPR data-handling controls.
- Ensure platform capacity is sized and load-tested for at least the documented 12x sales peak plus agreed headroom.
    6. Improve monolith safety while it remains live (depends on: 2, 4, 5)
       Stabilise the monolith so it can safely coexist with extracted services for most of the programme. The monolith remains a production dependency and needs the same operational discipline as new services.

- Add a modularity boundary map and enforce it with architecture tests, package rules, code ownership, and mandatory reviews for cross-module changes.
- Wrap high-risk database access behind repository or application interfaces, beginning with areas selected for extraction.
- Introduce expand-contract database migration rules. Additive, backward-compatible changes deploy first; destructive changes require evidence that all readers have moved.
- Raise automated regression coverage around critical journeys before touching them, using API, integration, and end-to-end tests rather than relying only on unit tests.
- Add feature flags and kill switches around all new monolith-to-service integrations.
- Reduce the 30-minute maintenance dependency by proving online deployment procedures, connection draining, backward-compatible schema releases, and zero-downtime smoke tests.
    7. Implement integration, event, and data-transition patterns (depends on: 3, 5, 6)
       Create reusable patterns for safe coexistence between the monolith and services. This is the core mechanism for reversible migration without dual-write corruption.

- Introduce an event backbone and schema registry or equivalent governance, with versioned events, retention policies, dead-letter handling, replay procedures, and consumer ownership.
- Implement transactional outbox publishing in the monolith and each service. Events are committed with source data and delivered asynchronously with deduplication.
- Provide change-data-capture only where an outbox cannot initially be added, with monitoring and a time-bound plan to replace it.
- Build a replication and reconciliation framework that compares source and target counts, hashes, business totals, lag, and exception records.
- Standardise anti-corruption adapters so services do not inherit monolith-specific data shapes and semantics.
- Define transition states for each entity: monolith-owned, replicated read model, dual-read validation, service-owned with monolith compatibility adapter, and legacy-retired.
    8. Create quality, performance, and release assurance (depends on: 2, 4, 5, 7)
       Replace confidence based on a fortnightly monolith release with automated evidence for each independently deployed component. Focus first on revenue-critical and migration-affected flows.

- Build a production-like test environment with anonymised data, external-provider simulators, warehouse-file simulators, and repeatable country, currency, language, tax, and promotion test fixtures.
- Establish consumer-driven API and event contract tests. Producers may not release breaking changes until consumers have migrated or compatibility periods expire.
- Create end-to-end tests for browse-to-order, guest and registered checkout, payment success and failure, cancellation, return, refund, stock changes, loyalty, and back-office operations.
- Implement load, soak, spike, chaos, and failover tests using the observed 12x sale profile. Run them before every traffic expansion.
- Use shadow execution for high-risk decisions. Compare service and monolith outputs without changing customer outcomes.
- Set release gates for security, contracts, performance, observability, rollback rehearsal, and business reconciliation.
    9. Select and sequence extraction waves (depends on: 2, 3, 8)
       Prioritise small, low-coupling seams first, then use the resulting capabilities for harder domains. Pricing, promotions, checkout, and core order ownership are deliberately not first-wave candidates.

- Wave 1: edge routing, read-only catalogue API, search, and selected back-office read/reporting capabilities.
- Wave 2: inventory availability read model and warehouse integration adapter, while preserving the current order and stock authority initially.
- Wave 3: customer profile and selected loyalty read/write capabilities, subject to GDPR and identity constraints.
- Wave 4: order query model, notification or non-core order workflow, and returns workflow where process boundaries are confirmed.
- Wave 5: cart and checkout façade components, followed by payment-provider adapters only after reliability evidence is sufficient.
- Treat pricing and promotions as a dedicated discovery-and-modernisation stream. Extract only verified, bounded slices after exhaustive parity testing; retain the monolith engine behind an API if full extraction is not safe within 12 months.
- Define per-wave entry criteria, exit criteria, capacity allocation, and a no-go rule for work that would cross a sales protection window.
    10. Introduce edge routing and façade interfaces (depends on: 4, 5, 6, 8)
       Decouple channels from monolith internals before extracting business capabilities. Web, mobile, and back-office clients must use stable, versioned interfaces rather than service-specific implementation details.

- Place an API gateway or backend-for-frontend layer in front of existing endpoints without changing functional behaviour.
- Route by path, tenant/country, customer cohort, feature flag, and percentage of traffic. Default routing remains to the monolith.
- Add authentication, authorisation, rate limiting, request validation, response compatibility, correlation IDs, and traffic dashboards at the edge.
- Preserve mobile API compatibility through versioning and adapter endpoints. Do not force a mobile release as a prerequisite for backend extraction.
- Implement instant route rollback to the monolith, including tested handling for sessions, carts, cached responses, and in-flight requests.
- Measure baseline response equivalence and latency overhead before moving any business endpoint.
    11. Extract catalogue read API and modern search (depends on: 7, 8, 9, 10)
       Deliver the first customer-facing extraction through read-heavy capabilities with clear fallback paths. This validates the platform, routing, replication, and operational model without changing transaction ownership.

- Build a catalogue read service fed from monolith-owned catalogue data through outbox or controlled replication.
- Replace nightly-only Lucene rebuilding with an independently operated search service that supports incremental index updates, aliases, blue/green indexes, and rapid rollback to the existing index.
- Run catalogue and search in shadow mode. Compare product availability, locale content, ranking, facets, response time, and zero-result rates against current behaviour.
- Shift traffic gradually by country and cohort. Keep the monolith catalogue/search route live until parity and peak tests pass.
- Introduce cache policies, invalidation events, stale-data limits, and cache-bypass operational controls.
- Do not make the new search service authoritative for price or stock. It displays explicitly versioned read models from their owning domains.
    12. Modernise inventory integration and availability reads (depends on: 7, 8, 9, 10)
       Separate warehouse file exchange from customer-facing inventory reads while preserving warehouse and order-system correctness. Inventory changes are operationally sensitive and require explicit freshness semantics.

- Build a warehouse integration adapter that validates, records, deduplicates, and acknowledges inbound and outbound files without changing warehouse contracts initially.
- Publish inventory-change events and create an availability read model for storefront and search use.
- Define country and fulfilment-node stock semantics, safety-stock rules, oversell tolerance, freshness targets, and customer messaging for stale or unavailable stock.
- Shadow-compare the new availability result with the monolith for all products and warehouses. Reconcile every discrepancy before traffic expansion.
- Preserve current monolith stock reservation and allocation authority until order and inventory ownership boundaries are fully designed.
- Provide an immediate fallback to monolith availability reads and a replayable file-processing recovery process.
    13. Discover and contain pricing and promotions (depends on: 2, 6, 7, 8, 9, 10)
       Treat pricing and promotions as the highest-risk business capability. First make its behaviour observable and testable; do not attempt a big-bang rewrite based on incomplete knowledge.

- Form a dedicated cross-functional squad with senior engineers, merchandising, finance, country representatives, customer support, and QA.
- Inventory all rules, stored procedures, configuration tables, overrides, scheduled jobs, manual back-office actions, and country-specific exceptions.
- Capture real production decision inputs and outputs into a privacy-safe decision log. Build a golden-master corpus covering products, customer segments, countries, currencies, dates, carts, vouchers, stacking, tax, and edge cases.
- Put the existing engine behind a versioned pricing façade. New callers use the façade even while it delegates to monolith logic.
- Build a new rules-evaluation candidate service only for well-understood rule slices. Shadow-evaluate and compare exact price, discount, explanation, and latency before any customer exposure.
- Require business sign-off, discrepancy classification, and financial-impact analysis before moving each rule slice. Keep a per-slice route-back switch to the legacy engine.
    14. Extract customer and loyalty capabilities safely (depends on: 7, 8, 9, 10)
       Move customer-facing identity-adjacent data only after privacy, consent, and data ownership are clear. Avoid introducing inconsistent account state across countries and channels.

- Define the canonical customer identifier, consent model, data-retention rules, subject-access and deletion workflows, and access-control model.
- Start with a replicated customer profile read service, then migrate bounded profile writes through a façade with idempotency and audit trails.
- Move loyalty functions in small slices, such as balance inquiry before accrual or redemption, using a ledger model and reconciliation against legacy balances.
- Support account-session compatibility across web, mobile, monolith, and new services throughout the transition.
- Reconcile customer records, consent states, and loyalty balances daily during migration. Route exceptions to trained operations staff.
- Retain a compatibility adapter for legacy back-office functions until those workflows are migrated or retired.
    15. Extract order views and bounded post-order workflows (depends on: 7, 8, 9, 10, 14)
       Create independently deployable order-related value without prematurely splitting the transactional checkout path. Start with event-driven reads and post-order processes that can tolerate asynchronous integration.

- Publish reliable order lifecycle events from the monolith using the outbox pattern.
- Build an order query service for customer-service, customer self-service, notifications, and selected back-office views. Validate it against monolith order history and live state.
- Extract bounded workflows such as notifications, selected return initiation, return-status tracking, and non-financial order enrichment where ownership is explicit.
- Preserve monolith order creation, payment capture coordination, cancellation authority, refund authority, and warehouse order export until their transition design is approved.
- Implement reconciliation for order counts, states, refunds, returns, notification delivery, and event lag.
- Ensure every new order-facing view identifies source freshness and has a monolith fallback for support staff.
    16. Create cart, checkout, and payment transition architecture (depends on: 7, 8, 9, 10, 11, 12, 13, 15)
       Prepare the revenue-critical transactional path through façade-first migration, exhaustive provider testing, and progressive traffic control. This stage must not force immediate service ownership transfer.

- Define cart identity, guest-to-account merge rules, session persistence, currency and country transitions, promotion snapshots, inventory checks, and checkout idempotency keys.
- Introduce a checkout façade that initially delegates to the monolith. Route storefront and mobile gradually while maintaining response and error compatibility.
- Isolate each payment provider behind versioned adapters with token handling, webhook verification, idempotent authorisation and capture, retry policy, reconciliation, and provider-specific fallback behaviour.
- Build a payment ledger and daily reconciliation process covering authorisations, captures, refunds, chargebacks, provider settlements, and orders.
- Shadow-run checkout orchestration and payment-adapter decisions where possible. Use provider test environments and controlled internal cohorts before customer traffic.
- Do not split the final order-creation transaction until failure-mode analysis, compensating actions, support procedures, and sale-peak load tests demonstrate acceptable risk.
    17. Transfer ownership through controlled data cutovers (depends on: 7, 8, 11, 12, 13, 14, 15, 16)
       Move write ownership one entity group at a time after services have proven read parity and operational maturity. Each cutover is a reversible state transition, not a one-time database migration.

- For each entity, document source of truth, writer cutover sequence, replication direction, API consumers, data-retention obligations, reconciliation rules, and rollback point.
- Use expand-contract schemas, backfills with checksums, change capture or outbox replication, dual-read validation, and carefully bounded write cutovers.
- Avoid unrestricted dual writes. During transition, route writes through one command owner that publishes changes reliably to dependent systems.
- Reconcile continuously by identifiers, row counts, hashes, financial totals, stock totals, and business state transitions. Define thresholds that automatically halt traffic expansion.
- Retain legacy data read access and compatibility APIs until all consumers are migrated and the defined observation period has passed.
- Schedule any high-risk ownership cutover outside sales protection windows, with an approved rollback rehearsal and staffed hypercare period.
    18. Execute progressive traffic migration and rollback drills (depends on: 4, 8, 10, 11, 12, 13, 14, 15, 16, 17)
       Move production traffic only through measured, reversible increments. Every migration uses the same operational playbook regardless of domain.

- Progress through dark launch, shadow comparison, employee cohort, low-risk country or cohort, 1%, 5%, 25%, 50%, and full traffic stages where appropriate.
- Define quantitative promotion criteria for each stage: error rate, latency, conversion, search quality, price parity, payment approval rate, order completion, inventory discrepancy, support contacts, and reconciliation lag.
- Automate route rollback and validate it with game days. Rollback must restore a known compatible route without data loss or customer-visible duplicate operations.
- Run failure injection for dependency loss, event delay, duplicate messages, provider timeout, cache failure, database failover, and warehouse-file replay.
- Maintain staffed hypercare after each material expansion, with business, support, and engineering representatives able to pause or reverse rollout.
- Freeze traffic increases before sales protection windows. Use those windows only for monitoring, capacity verification, defect fixes with approved exceptions, and rehearsed rollback readiness.
    19. Prepare peak-season resilience and capacity certification (depends on: 4, 5, 8, 11, 12, 13, 16, 18)
       Certify both the hybrid estate and fallback paths for January and July sales. A service is not production-ready if its rollback target cannot sustain the traffic it might receive.

- Forecast peak demand by country, channel, page type, checkout step, payment provider, product launch, and warehouse activity.
- Load-test the full hybrid path at at least 12x observed baseline plus agreed headroom, including gateway, caches, monolith, services, event platform, databases, search, payment adapters, and warehouse integration.
- Test traffic reversion from each service to monolith and confirm that the monolith, database, and legacy search can absorb the reverted load.
- Establish capacity reservations, autoscaling limits, database connection limits, cache warm-up procedures, provider rate-limit agreements, and operational staffing plans.
- Conduct sale-day simulations, incident command exercises, communications rehearsals, and business-continuity tests with payment providers and warehouse stakeholders.
- Obtain formal peak-readiness sign-off from engineering, operations, commerce, finance, warehouse, and customer support.
    20. Retire legacy paths and establish steady-state service governance (depends on: 17, 18, 19)
       Conclude the 12-month programme by removing only proven-obsolete paths and making service ownership sustainable. Retain legacy components where removal would create unjustified business risk.

- Decommission monolith endpoints, batch jobs, Lucene components, table access paths, and stored procedures only after consumer inventory, data archival, reconciliation, and rollback-retention periods are complete.
- Remove temporary replication and compatibility adapters in controlled releases. Update runbooks, diagrams, recovery procedures, and ownership records at the same time.
- Measure and reduce residual monolith coupling, direct database access, synchronous dependency chains, event lag, and operational toil.
- Establish quarterly architecture reviews, API and event lifecycle governance, service scorecards, dependency upgrade policies, resilience testing, and disaster-recovery exercises.
- Conduct a post-migration review against business outcomes, incident history, delivery lead time, cost, and peak performance. Prioritise any remaining pricing, checkout, order, or database decomposition work as a funded follow-on roadmap.
- Confirm that independently deployable services have named owners, on-call coverage, SLOs, operational documentation, and tested rollback or recovery procedures.

  Previous Proposal 3 (ID: b39f2a46-559f-4920-9664-540fcdb612f4, Agent: grok-4.6_initial_3, LLM: xai/grok-4.6):
    Estimated Complexity: high
    Success Metrics: - Zero unplanned downtime attributed to migration work across the 12 months.
- Every production cutover has a practised rollback that restores the previous path in minutes.
- January and July peak capacity at or above today's 12x headroom, with no extra error-budget burn on search, cart, checkout or payments.
- Feature throughput stays at or above the current two-week train; no programme-wide feature freeze.
- At least search, catalogue, identity, inventory, pricing, cart, checkout and OMS deploy independently of the monolith artefact.
- Dual-run mismatch rate for prices and stock below an agreed threshold before each traffic shift (target: 0 on money paths).
- Golden-journey pass rate 100% on critical paths before and after each cutover.
- Monolith database coupling reduced: no new cross-context joins; stored-procedure call volume on extracted domains at zero after ownership transfer.
- Mean time to revert a bad service release under 10 minutes via flags or routing.
    Steps (25):
    1. Charter, governance and non-negotiables
       Write a short **migration charter** that product, ops, finance and all five teams sign.

Feature work never stops. Only production risk is constrained.

- Name one accountable migration lead and a weekly steering forum.
- Ban big-bang rewrites, shared-database-first splits and un-reversible cutovers.
- Require a tested rollback for every production step.
- Keep the two-week monolith release train for features until a domain is fully extracted.
    2. Peak calendar and freeze protocol (depends on: 1)
       Protect **January and July** sales with hard engineering blackouts.

No extractions, schema splits or traffic switches in the six weeks before a sale or the two weeks after, unless they are already proven and idle.

- Publish the 12-month calendar in week one.
- Freeze means no new migration risk, not a feature freeze.
- Require a peak capacity rehearsal before each blackout.
- Give ops a veto on any change that could affect checkout, payments, stock or search.
    3. Baseline architecture, data and SLOs (depends on: 1)
       Measure the live system before changing it.

Build a factual map of the 2M-line monolith, the 1.2 TB database and the real traffic shape.

- Trace the top 30 user journeys and the 350 tables they touch.
- Record p50/p95/p99, error rates and 12x peak headroom per journey.
- Inventory stored procedures, cross-module joins and file exchanges.
- Tag every endpoint used by the storefront, mobile app and back-office.
    4. Delivery platform, flags and progressive delivery (depends on: 1)
       Give every team a **safe way to ship** without the 30-minute maintenance window.

New work deploys behind flags. Old work stays on the existing train until it is ready.

- Add feature flags, weighted routing and instant revert at the edge.
- Build CI that can later publish one artefact per service.
- Keep Java 8 on the monolith. Start new services on a current LTS.
- Provide preview environments that replay production-like traffic.
    5. Observability and error budgets (depends on: 3, 4)
       Instrument the monolith as if it were already many services.

You cannot extract what you cannot see.

- Add distributed tracing, RED metrics and structured logs with correlation IDs.
- Define SLOs for search, PDP, cart, checkout, payments and back-office.
- Page on error-budget burn, not on CPU.
- Dashboards must show monolith vs new service side by side for every cutover.
    6. Safety net: journeys, contracts and load (depends on: 3)
       Raise the net where extraction will cut.

Unit coverage at 25% is not enough. Protect behaviour, not lines.

- Record golden journeys for browse, price, cart, checkout, order, return and loyalty.
- Add contract tests on every mobile and storefront endpoint.
- Capture characterization tests around stored procedures before moving them.
- Automate a 12x peak load test and run it before each sale and each major cutover.
    7. Bounded contexts and extraction backlog (depends on: 3)
       Draw domain boundaries from the business, not from the package tree.

Sequence work by **risk and coupling**, not by fashion.

- Contexts: identity, catalogue, search, pricing, inventory, cart, checkout, orders, returns, loyalty, back-office.
- Extract read-mostly and already-async seams first (search, inventory files).
- Leave pricing and checkout until dual-run and reconciliation exist.
- Rank a 12-month backlog with a rollback story on every item.
    8. Team operating model without a freeze (depends on: 1, 7)
       Keep five domain teams. Stop treating the repo as a single ownership blob.

Each team ships features in the monolith **and** prepares its future service.

- Assign a service to own per team, plus a shared platform pair.
- Code owners and module walls inside the current repository first.
- A small platform group owns gateway, flags, events, CI and data tooling.
- Product still plans features; migration work is a percentage of each sprint, not a separate freeze.
    9. Modularise the monolith in place (depends on: 6, 7)
       Create seams before you create processes.

New code may not add cross-module joins or new stored-procedure coupling.

- Split packages by bounded context with compile-time walls.
- Replace in-process calls at boundaries with interfaces (branch by abstraction).
- Document and freeze the worst pricing and checkout internals; wrap them.
- Ban new features from reaching into another team's tables.
    10. Strangler facade and instant traffic rollback (depends on: 4, 5)
       Put a reverse proxy in front of every public and mobile endpoint.

Clients keep the same URLs. You choose monolith or service per route and per percentage.

- Preserve headers, sessions, cookies and the four languages.
- Shadow traffic before any live percentage.
- Rollback is a route change, not a redeploy, and must complete in minutes.
- Storefront SSR and the mobile app stay compatible until a later BFF if needed.
    11. Events, outbox and CDC backbone (depends on: 5, 9)
       Give the monolith a **reversible integration spine**.

Services must not call each other's databases. They subscribe to facts.

- Add an outbox in the same Postgres transaction as business writes.
- CDC from the monolith for tables you do not yet own.
- Standard event names for product, price, stock, customer, order and return.
- Idempotent consumers and a dead-letter process before the first extraction.
    12. Data-change playbook: dual-write, reconcile, roll back (depends on: 11)
       Treat every data move as a campaign with an abort switch.

The 1.2 TB database stays the system of record until a service proves otherwise.

- Dual-write with the monolith write winning on conflict during trial.
- Nightly and continuous reconciliation with row-level diffs.
- Never cut stored procedures until logic has an equivalent test harness.
- Rollback means stop writes to the new store and keep serving from Postgres.
    13. Extract search as the first service (depends on: 2, 8, 10, 11, 12)
       Replace the nightly Lucene rebuild with an independently deployed **search service**.

This is read-heavy, already eventually consistent, and off the payment path.

- Index from catalogue and price events, not from a nightly dump.
- Shadow queries against current Lucene until precision/recall match.
- Shift traffic 1% → 10% → 50% → 100% with instant route rollback.
- Keep the old index warm through the next sale as a cold standby.
    14. Extract catalogue read models (depends on: 13)
       Serve product, media and localisation from a catalogue service.

Writes can stay in the monolith until editors have a new path.

- Build country and language-specific read models for eight markets.
- Keep one product identity so pricing, stock and search stay aligned.
- Cut storefront and mobile read traffic via the strangler.
- Do not move merchandising tools until reads are stable.
    15. Extract identity, accounts and session (depends on: 8, 10, 12)
       Pull login, profile, addresses and session behind a dedicated service.

Mobile and web keep the same auth cookies or tokens during the switch.

- Migrate sessions without forced logouts.
- Dual-read loyalty points until that domain is extracted.
- GDPR/export and deletion flows must work in both systems.
- Rollback restores monolith auth with no password resets.
    16. Extract inventory and warehouse sync (depends on: 8, 11, 12)
       Replace the 15-minute file exchange with an inventory service that still talks to the warehouse.

The warehouse interface stays file-based until they can change. Your side becomes events.

- Service owns ATP, reservations and oversell rules.
- Adapter keeps the existing file contract so warehouse risk is zero.
- Cart and checkout read stock from the service via API or replica.
- Prove no extra oversell versus today's 15-minute lag before a sale.
    17. Pricing archaeology and dual-run harness (depends on: 6, 9)
       Do not extract the 200k-line pricing module until you can prove equivalence.

Nobody fully understands country rules. Tests must become the spec.

- Capture production price traces for all eight countries and three currencies.
- Build a harness that replays promotions, baskets and edge SKUs.
- Freeze behavioural snapshots; new promo features implement twice until cutover.
- Only then wrap pricing behind an interface inside the monolith.
    18. Extract pricing and promotions behind dual-run (depends on: 14, 17, 12)
       Run the new pricing service in **shadow** until it matches the monolith on live baskets.

Checkout keeps using monolith prices until the error budget is clean.

- Compare every quote; alert on any currency, tax or promo mismatch.
- Shift read traffic first, then write of promo usage.
- Keep the monolith engine deployable as rollback through the next two sales.
- Country-specific rules move last, one market at a time if needed.
    19. Extract cart (depends on: 15, 16, 18)
       Move the cart after identity, catalogue, stock and price reads are stable.

Cart is stateful. Lose no baskets during cutover.

- Dual-write carts; reconcile abandoned and active baskets.
- Preserve promo application using the dual-run price API.
- Session migration must survive app versions in the wild.
- Rollback reattaches baskets to the monolith cart tables.
    20. Extract checkout and payment orchestration (depends on: 19)
       Strangle checkout without touching the three payment providers in one step.

A thin orchestration service talks to existing provider integrations first.

- Keep PCI and provider contracts stable; wrap, do not rewrite.
- Idempotent order placement with an outbox to OMS.
- Canary by country and by payment method.
- Rollback is route-plus-flag; in-flight payments complete on the old path.
    21. Extract order management (depends on: 20)
       Move post-purchase order state once checkout emits reliable events.

OMS must survive 12x peaks and warehouse files.

- Order of record shifts only after reconciliation is clean for a full weekly cycle.
- Back-office screens can still read a projection while writes move.
- Returns and finance reports stay correct during dual-run.
- Keep monolith OMS as standby through one sale after cutover.
    22. Extract returns, loyalty and remaining back-office (depends on: 15, 21)
       Peel remaining domains once orders and identity are independent.

Staff of 300 must not get a big-bang UI change.

- Returns service consumes order events and drives refunds via payment facade.
- Loyalty becomes the owner of points with dual-write from checkout.
- Back-office gets BFFs or modular UIs per domain, not a new monolith.
- Train staff per screen group; keep old screens until the new ones match.
    23. Split data ownership and retire stored procedures (depends on: 16, 18, 21)
       Give each stable service its **own schema or database** only after traffic and reconciliation are boring.

Shared Postgres is allowed during transition. It is not the end state.

- Move table ownership context by context; no cross-service joins.
- Rewrite stored procedures into service code with the characterization harness.
- Shrink the 1.2 TB monolith database as tables go dark.
- Rollback is restoring replication to the monolith copy, practised in staging.
    24. Independent deploy pipelines and repository split (depends on: 8, 23)
       When a service is independently releasable, stop bundling it into the fortnightly artefact.

Teams ship on demand with automated checks and progressive delivery.

- One pipeline per service: test, canary, promote, revert.
- Split repos only after module walls and CI already work in the monorepo.
- Contract tests gate consumer and provider deploys.
- The remaining monolith keeps the old two-week train until it is small.
    25. Peak rehearsals, chaos and residual shrink (depends on: 2, 22, 24)
       Prove **12x capacity** on the mixed architecture before each sale.

Then keep shrinking the monolith so it is a shell, not a risk.

- Game-days: provider failure, CDC lag, flag revert, search fallback, stock file delay.
- Scale tests on checkout, search and inventory with production-sized data.
- Delete dead modules, jobs and tables only after two quiet weeks.
- End state: independently deployable services, instant rollback still in place, no unplanned downtime attributed to the programme.

  Previous Proposal 4 (ID: 9219a465-1887-4823-81a8-3cbfc3bb545b, Agent: deepseek-v4-pro_initial_4, LLM: deepseek/deepseek-v4-pro):
    Estimated Complexity: high
    Success Metrics: - 100% of critical customer journeys remain within SLOs during migration; no unplanned downtime outside planned windows.
- Every extraction step has a rehearsed rollback path that restores monolith behaviour in under 5 minutes.
- Peak-season capacity maintained: January and July sales complete without capacity-related errors, with peak traffic at least 12x baseline and error rate <= 0.1%.
- By month 12, at least 8 core business capabilities are deployed as independently deployable services from separate repositories with separate data ownership.
- Monolith code is reduced by at least 60%, and the remaining monolith no longer owns migrated data or executes migrated stored procedures.
- Deployment frequency increases from one release every two weeks to daily per service; lead time for changes decreases from weeks to hours.
- Test coverage on changed code reaches at least 80%; critical pricing and checkout paths have contract and parity tests.
- Zero data loss or irreversible data corruption during migration; reconciliation discrepancies are below 0.01% of records.
- Feature delivery velocity remains at least equal to pre-migration levels; no feature freeze is imposed.
- No cross-service direct database joins remain for migrated capabilities; all service data access happens through APIs or events.
    Steps (19):
    1. Baseline and decompose the monolith into bounded contexts
       Capture the current behaviour, data model, and operational risks before changing anything. The output is a shared map that justifies every later cutover.
- Inventory all modules, endpoints, database tables, stored procedures, cross-module joins, external integrations, and batch jobs.
- Map business capabilities to bounded contexts and identify candidate service seams and data owners.
- Record every country/currency/language variation, especially the 200k-line pricing and promotions module.
- Capture the peak-season calendar, current deployment windows, known failure modes, and rollback mechanisms.
- Create a risk register with blast radius and rollback criteria for each candidate extraction.
    2. Define target service architecture and migration sequence (depends on: 1)
       Agree the target state and the guardrails before building any new service.
- Publish target decomposition: storefront, catalogue/search, pricing/promotions, cart/checkout, orders, inventory, customers/loyalty, returns, back-office.
- Define synchronous APIs, asynchronous events, idempotency, retries, sagas, and eventual consistency where required.
- Define data ownership and database-per-service strategy; prohibit cross-service joins and direct access to another service's tables.
- Define API versioning, security, tenancy, and country-specific routing.
- Choose migration sequence: start with low-risk read-heavy capabilities and delay peak-sensitive cutovers until outside sales windows.
- Set the rollback requirement: every change must be behind a flag or reversible migration with rehearsed rollback.
    3. Establish observability, SLOs and production load testing (depends on: 1)
       Make the current system measurable so cutovers are based on data, not hope.
- Add structured logs, metrics, and distributed tracing to the monolith and future services.
- Define SLOs and error budgets for storefront, catalogue, cart, checkout, payments, and order management.
- Add synthetic transactions and real-user monitoring for 8 countries, 3 currencies, and 4 languages.
- Build a performance test environment that replays production-like traffic at peak 12x volume.
- Create dashboards for golden signals, slow queries, stored procedure hotspots, and cache/index health.
    4. Build zero-downtime CI/CD and database migration automation (depends on: 2)
       This is the safety rail for every later step: frequent, reversible, low-risk deployments.
- Replace the biweekly single-artifact release with a pipeline supporting per-service builds, automated tests, security scans, and deployment.
- Introduce canary and blue-green deployment with automated rollback based on SLOs and error budgets.
- Add expand/contract database migration patterns: first add new schema, dual-write or synchronise, switch reads, then remove old schema in a later release.
- Ensure every service change is independently deployable in minutes, with no planned maintenance window.
- Use infrastructure-as-code and immutable artifacts for all environments.
    5. Strengthen tests and add contract testing before cutting seams (depends on: 3, 4)
       Raise confidence in behaviour without freezing features, focusing on seams to be extracted.
- Build characterization tests around current API endpoints, stored procedures, pricing rules, and checkout flows.
- Add consumer-driven contract tests between the monolith and new services.
- Introduce mutation testing and enforce at least 80% coverage on changed code.
- Add data-migration tests, reconciliation tests, and performance regression gates to CI/CD.
- Keep a long-running dual-read and diff harness for later services.
    6. Introduce traffic routing and feature flag platform (depends on: 4, 5)
       Enable gradual migration and instant rollback without redeploying the entire monolith.
- Deploy a feature flag system and edge/API gateway that can route traffic by customer, country, currency, language, percentage, and header.
- Add dark-launch capability to send shadow traffic to new services while the monolith remains source of truth.
- Implement kill switches that revert to monolith paths in one action.
- Integrate flags with SLO dashboards and deployment rollback.
    7. Extract customer accounts and loyalty as pilot service (depends on: 2, 3, 4, 5, 6)
       Prove the extraction playbook on a well-bounded, lower-risk capability before touching the most complex modules.
- Create a customer service owning customer, address, and loyalty data; expose a REST API with the same contracts.
- Move related monolith code behind an anti-corruption layer; run dual-writes or CDC to keep data in sync.
- Use expand/contract database migration: retain monolith tables temporarily, synchronise with the service, then switch reads/writes by flag.
- Launch to a small country and a small traffic percentage; monitor SLOs and rollback if errors exceed the error budget.
- Use the pilot to refine templates, runbooks, and training for other teams.
    8. Extract catalogue and search into a dedicated service (depends on: 3, 4, 5, 6, 7)
       Move the read-heavy catalogue and search path first, as it is valuable and relatively safe if done in shadow mode.
- Build a catalogue/search service that owns product, category, and search data; maintain the Lucene index within the service or via a dedicated index.
- Synchronise catalogue data from the monolith through CDC or events; stop cross-module joins.
- Serve storefront and mobile via the new catalogue/search API; run shadow reads against the monolith and compare.
- Route reads progressively by country and language and validate search quality, latency, and conversion.
- Keep the monolith fallback and flag-based rollback until after the peak readiness gate.
    9. Extract pricing and promotions with dual-run comparison (depends on: 7, 8)
       The most complex module; migration must be based on observed behavioural equivalence.
- Build a pricing/promotions service with country-specific rules as versioned configuration or domain rules.
- Run the new service in shadow mode on all checkout/cart/catalogue calls and compare every calculation with the monolith for months before cutover.
- Treat any divergence as a defect; require 100% parity on sampled and historical promotion scenarios before routing live traffic.
- Expose a pricing API and route live reads/writes only by country and promotion type, with immediate rollback.
- Keep the monolith promotion engine available until after all peak seasons.
    10. Extract inventory service and modernise warehouse integration (depends on: 7)
       Replace the 15-minute file exchange with safer, event-driven inventory updates while keeping the old path as fallback.
- Build an inventory service owning stock levels, reservations, and warehouse sync logic.
- Integrate with the warehouse system via API or events and keep the file exchange running in parallel for dual sync.
- Expose inventory availability and reservation APIs for cart, checkout, and back-office.
- Run reconciliation between the old file batch and the new event flow for all SKUs; eliminate divergence before cutover.
- Route inventory consumers to the service progressively, maintaining the monolith fallback.
    11. Extract cart and checkout service (depends on: 7, 8, 9, 10)
       Move the highest-value transaction path only after its dependencies are available and proven.
- Build a cart/checkout service that owns cart state and checkout workflow; it calls customer, catalogue, pricing, and inventory APIs with fallbacks.
- Integrate the three payment providers through adapters; implement idempotency, retries, and reconciliation.
- Use saga or orchestration for payment, inventory reservation, and order creation.
- Route by country, currency, and traffic percentage; start with one payment provider and one country.
- Rehearse rollback to monolith checkout and validate that no cart or payment is lost.
    12. Peak readiness gate before first sales peak (depends on: 7, 8, 9, 10, 11)
       Protect the first peak by freezing risky cutovers while allowing normal feature work through flags.
- Freeze new service cutovers and irreversible data migrations for four weeks before and during the peak.
- Run production-like load tests at 12x baseline with monolith and new services in their current routing ratios.
- Rehearse rollback for every extracted service and confirm the monolith fallback handles full load.
- Pre-scale infrastructure to at least 30% above expected peak.
- Keep on-call and war-room runbooks ready; certify only if all SLOs pass in load tests.
    13. Extract order management service after first peak (depends on: 11, 12)
       Move order persistence and lifecycle after the first peak, using events from checkout and inventory.
- Build an order service owning orders and order lines; consume order-placed events from checkout and payment.
- Replace monolith order creation and status update code behind flags.
- Backfill historical orders into the service and run reconciliation.
- Route order read/write traffic progressively; maintain the monolith fallback.
- Ensure returns and customer service integration remains consistent.
    14. Extract returns service (depends on: 13)
       Move returns and refunds out of the monolith once order and inventory services are stable.
- Build a returns service owning return requests, labels, refund settlements, and status.
- Integrate with order, inventory, and payment services via APIs and events.
- Migrate business rules country-by-country with dual-run comparison.
- Keep the monolith fallback and rollback for all return journeys.
    15. Extract back-office capabilities (depends on: 13, 14)
       Decouple internal staff tools from the monolith so operations run on the new services.
- Build a new back-office UI or modular back-office services that use the same APIs as storefront services.
- Migrate catalogue, pricing, inventory, order, customer, and returns management screens incrementally.
- Route the 300 staff users per function and country; keep the old monolith back-office available during transition.
- Automate manual processes and remove direct SQL access to migrated data.
    16. Peak readiness gate before second sales peak (depends on: 13, 14, 15)
       Repeat the readiness gate for the second peak with more services in production.
- Freeze cutovers and irreversible migrations four weeks before and during the peak.
- Run full production-like load test at 12x peak with current routing ratios.
- Rehearse rollback for order management, returns, and back-office services.
- Verify capacity headroom and failover to monolith if required.
- Certify only if all critical SLOs are met under peak stress.
    17. Decommission monolith code and data ownership for migrated modules (depends on: 16)
       Remove the old implementation only after both peaks and after all services have proven parity.
- Remove feature flags and dark-launch paths for customer, catalogue, pricing, inventory, cart/checkout, orders, and returns.
- Drop or archive monolith tables and stored procedures for migrated modules after data reconciliation.
- Delete monolith code paths and reduce monolith size; verify no cross-module joins remain.
- Run final reconciliation and zero-downtime data archival.
    18. Final cutover, contract freeze and monolith retirement (depends on: 17)
       Complete the strangler migration so the monolith stops serving business traffic.
- Switch any remaining traffic to services and remove edge routing to the monolith.
- Decommission monolith deployments; maintain a read-only archive for audit.
- Finalise API contracts, versioning, and service ownership.
- Complete security reviews and production runbooks for all services.
    19. Continuous improvement and governance handover (depends on: 18)
       Make the new architecture the default and keep it safe after migration.
- Embed service ownership: each team owns build, deploy, and operate with SLOs.
- Review cost, performance, and reliability; tune autoscaling and capacity for the next peak.
- Update architecture decision records, runbooks, and onboarding material.
- Establish a light-touch governance board for API and data contracts.

  Previous Proposal 5 (ID: 733858cb-4dad-4bb6-85c9-79a3e6d968d9, Agent: qwen3.8-max_initial_5, LLM: alibaba/qwen3.8-max):
    Estimated Complexity: high
    Success Metrics: - Zero unplanned downtime throughout the 12-month migration; all maintenance performed via blue-green or canary cutover.
- Every extraction step is reversible within 5 minutes via feature-flag rollback, validated at least once in production before full cutover.
- Deployment frequency increases from bi-weekly to at least daily per service by month 4.
- Peak-load capacity sustained at 12× normal traffic (≈ 480 000 orders/day) with p99 latency ≤ 800 ms for checkout and ≤ 400 ms for storefront during January and July sales.
- Test coverage on all migrated code paths reaches ≥ 80 %; contract tests exist for every inter-service boundary.
- Monolith codebase reduced from 2 M lines to 0 lines in production by end of month 12.
- All 350 tables are owned by exactly one service; zero cross-service direct database joins remain.
- The three payment providers maintain ≥ 99.95 % successful transaction rate throughout the migration.
- Back-office availability for 300 staff ≥ 99.9 % during business hours across all 8 countries.
- Mean time to recovery (MTTR) for any single-service incident ≤ 10 minutes.
- No degradation in order-accuracy rate (≥ 99.99 %) or inventory reconciliation accuracy (≥ 99.9 %) at any point during the migration.
- Customer-facing error rate (5xx) stays below 0.1 % across all 8 countries, 3 currencies, and 4 languages throughout the programme.
    Steps (20):
    1. Full-Scope Discovery and Dependency Mapping
       Perform a **complete technical and organisational audit** of the monolith before any code changes.

- Run static-analysis tools (jQAssistant, ArchUnit, custom SQL scripts) to produce a module-level dependency graph across all 2 M lines of Java and all 350 PostgreSQL tables.
- Catalogue every stored procedure, trigger, and cross-module join; classify each as *local*, *cross-module read*, or *cross-module write*.
- Interview each of the five teams to document tribal knowledge, especially the pricing & promotions rules (200 K lines, country-specific logic).
- Map all external integrations: three payment providers, warehouse file exchange, mobile-app endpoints, back-office UI routes.
- Record current performance baselines: p50 / p95 / p99 latency per endpoint, throughput, DB query plans for the top-100 queries.
- Deliverable: a living architecture dossier stored in a shared wiki, updated throughout the migration.
    2. Build CI/CD Pipelines and Feature-Flag Platform (depends on: 1)
       Create the **deployment and release-safety infrastructure** that every later step depends on.

- Stand up a CI/CD stack (e.g. GitLab CI or GitHub Actions → ArgoCD) capable of building, testing, and deploying individual modules independently.
- Introduce a feature-flag platform (LaunchDarkly, Flagsmith, or Unleash) wired into the monolith via a thin SDK; every new or changed code path ships behind a flag.
- Define branching strategy: one repo per future service, plus the existing monorepo during the transition period.
- Automate canary and blue-green deployment patterns so every release can be rolled back in under five minutes.
- Target: reduce the two-week release cycle to **daily deployable** by end of this step.
    3. Establish Observability, Tracing, and SLO Baseline (depends on: 1)
       Instrument the monolith so that **every subsequent extraction is measurable** and regressions are caught within minutes.

- Deploy OpenTelemetry agents across all application nodes; export traces, metrics, and structured logs to a central stack (Grafana Tempo + Prometheus + Loki, or Datadog).
- Define SLOs per domain: storefront p99 < 400 ms, checkout p99 < 1.2 s, search p95 < 300 ms, back-office p95 < 2 s.
- Build real-time dashboards per SLO with alerting thresholds; wire alerts to on-call rotation.
- Implement synthetic transaction monitoring covering the critical user journeys (browse → cart → checkout → payment → confirmation) across all 8 countries, 3 currencies, and 4 languages.
- Establish an error-budget policy: any extraction step that breaches its SLO budget is automatically rolled back.
    4. Automated Testing Uplift and Contract-Test Foundation (depends on: 2)
       Raise test coverage from **25 % to at least 60 %** on the paths that will be touched first, and introduce contract testing.

- Use mutation testing (PIT) to identify the highest-risk untested paths; prioritise checkout, payment, and inventory flows.
- Add integration tests using Testcontainers with a seeded copy of the production schema.
- Introduce Pact (or Spring Cloud Contract) for consumer-driven contract tests between every pair of modules that will become separate services.
- Build a regression suite of end-to-end smoke tests runnable in < 15 minutes, executed on every deploy.
- Define a policy: no extraction proceeds unless the affected module reaches the agreed coverage threshold.
    5. Team Topology Realignment and Governance Model (depends on: 1)
       Reorganise the five teams into **stream-aligned, domain-owned squads** and agree on governance rules for the migration.

- Map each team to a bounded context: (1) Storefront & Search, (2) Pricing & Promotions, (3) Cart, Checkout & Payments, (4) Order Management, Inventory & Returns, (5) Customer, Loyalty & Back-Office.
- Assign a Platform/Enablement guild (2–3 senior engineers drawn across teams) responsible for shared infra, libraries, and cross-cutting concerns.
- Agree on API governance: versioning policy (URL-path major, header minor), deprecation window (minimum 90 days), and an internal API catalogue.
- Set up a weekly cross-team architecture sync and a migration-risk register reviewed every sprint.
- Define the rollback decision tree: who can trigger a rollback, under what SLO breach, and the communication protocol.
    6. Strangler-Fig Gateway and Anti-Corruption Layer (depends on: 2, 3)
       Deploy an **API gateway in front of the monolith** that will route traffic to either the legacy code or the new services, enabling incremental extraction.

- Place a reverse-proxy / service mesh layer (e.g. Kong, Envoy via Istio, or AWS ALB + App Mesh) in front of the existing load balancer.
- Implement an Anti-Corruption Layer (ACL) service that translates between the monolith's internal models and the new service APIs.
- Configure the gateway to route by URL pattern, header, or feature flag; default route goes to the monolith.
- Support traffic mirroring (shadow traffic) so new services can be validated against live production traffic before receiving real requests.
- All mobile-app and back-office traffic passes through the gateway from day one; server-rendered pages are proxied transparently.
    7. Database Decomposition Strategy and Shared-Data Refactor (depends on: 1, 4)
       Prepare the **1.2 TB PostgreSQL database** for eventual per-service ownership without a big-bang migration.

- Classify all 350 tables by bounded context using the dependency map from S1.
- Eliminate cross-module joins at the application layer first: replace them with service calls or denormalised read models.
- Convert stored procedures that span contexts into application-level logic behind the ACL; keep single-context procedures temporarily.
- Introduce an internal event log (outbox pattern) on the existing database: every state change publishes a row to an `outbox` table, later relayed to a message broker.
- Define the target data-ownership matrix: which service will own which tables, and which data will be replicated read-only.
- Plan a dual-write / change-data-capture (CDC) strategy using Debezium so that during transition both old and new stores stay consistent.
    8. Event-Driven Backbone and Async Messaging Layer (depends on: 6, 7)
       Stand up the **messaging infrastructure** that decouples services and replaces synchronous cross-module calls.

- Deploy Apache Kafka (or AWS MSK) with topics per bounded context: `catalogue-events`, `order-events`, `inventory-events`, `pricing-events`, `customer-events`.
- Implement the transactional outbox relay (Debezium → Kafka Connect) so the monolith can publish domain events without code changes to business logic.
- Define event schemas in a central Schema Registry (Avro / Protobuf) with backward-compatibility enforcement.
- Add idempotent consumer patterns and dead-letter queues from day one.
- Validate throughput: the backbone must sustain 12× peak (≈ 480 000 orders/day equivalent event volume) with headroom.
    9. Containerisation and Kubernetes Platform Readiness (depends on: 2, 3)
       Package the monolith and prepare a **Kubernetes-based runtime** for all future services.

- Dockerise the existing monolith (multi-stage build, slim JRE image) and deploy it to a Kubernetes cluster alongside the gateway.
- Provision namespaces per bounded context, with network policies enforcing that only the gateway and the ACL can reach the monolith.
- Configure horizontal pod autoscaling, pod disruption budgets, and resource quotas sized for 12× peak.
- Set up a service mesh (Istio or Linkerd) for mTLS, traffic splitting, circuit breaking, and retry policies.
- Run a load test replicating the January-sale profile (12× normal traffic) to validate the platform before any service extraction.
    10. Extract Customer Accounts and Loyalty Service (Wave 1) (depends on: 4, 6, 7, 8, 9)
       Carve out the **lowest-risk, well-bounded domain** first to validate the full extraction playbook.

- Build a new `customer-service` (Java 21 / Spring Boot 3 or Kotlin) exposing REST + gRPC APIs for registration, authentication, profile, and loyalty points.
- Migrate the relevant 15–20 tables to a dedicated PostgreSQL instance using the CDC dual-write pattern from S7.
- Place the service behind the ACL; route traffic via feature flags starting at 1 % → 10 % → 50 % → 100 % over two weeks.
- The monolith continues to serve as fallback; a single flag flip routes 100 % back.
- Validate contract tests, SLO dashboards, and rollback procedure end-to-end.
- This extraction serves as the **reference implementation** for all subsequent waves.
    11. Extract Catalogue and Search Service (Wave 2) (depends on: 10)
       Replace the nightly Lucene rebuild with a **real-time search and catalogue service**.

- Build a `catalogue-service` owning product data, categories, and media references; use CDC from the monolith DB during transition.
- Replace Lucene with Elasticsearch or OpenSearch; index updates driven by Kafka events instead of the nightly batch.
- Expose search and browse APIs through the gateway; server-rendered storefront pages call the new API via the ACL.
- Migrate in two sub-phases: (a) read-only catalogue and search behind flags, (b) write path (product updates from back-office) once reads are stable.
- Keep the legacy Lucene index warm for instant rollback for 60 days.
- Validate that search latency meets the p95 < 300 ms SLO across all 4 languages.
    12. Extract Inventory and Warehouse Sync Service (Wave 3) (depends on: 10)
       Isolate the **inventory domain and its 15-minute file-exchange** with the warehouse system.

- Build an `inventory-service` owning stock levels, reservations, and warehouse synchronisation.
- Replace the file-based exchange with an event-driven adapter: the service consumes warehouse updates via SFTP poll or API and publishes `inventory-updated` events to Kafka.
- During transition, run the adapter in parallel with the legacy file job; reconcile counts nightly.
- Checkout and order-management modules consume inventory availability via synchronous gRPC (with circuit breaker) and asynchronous events for reservation confirmations.
- Migrate stock tables using CDC; rollback path re-points reads to the monolith tables.
- Validate under 12× peak load: inventory checks must not become a bottleneck during flash sales.
    13. Deep Analysis and Rule Documentation for Pricing & Promotions (depends on: 1)
       Before touching the **most complex 200 K-line module**, invest in understanding and documenting its rules.

- Pair domain experts from each of the 8 country teams with developers to walk through every pricing rule, promotion type, and country-specific override.
- Produce a machine-readable rule catalogue (decision tables or a lightweight DSL) that captures all 200+ identified rules.
- Identify dead code, redundant branches, and rules that have not fired in the last 24 months (use production logging and feature-flag data).
- Classify rules into: (a) universal, (b) country-specific, (c) campaign/temporary.
- Define the target architecture: a `pricing-service` with a rules engine (Drools, Easy Rules, or a custom evaluation pipeline) externalised from application code.
- Deliverable: a signed-off rule specification document that all five teams agree represents current behaviour.
    14. Extract Pricing and Promotions Service (Wave 4) (depends on: 11, 12, 13)
       Rebuild the **highest-risk module** as an independent service using the documented rule set from S13.

- Build a `pricing-service` with a pluggable rules engine; encode the rule catalogue from S13 as configuration rather than hard-coded Java.
- Expose two API surfaces: synchronous price calculation (called by cart/checkout) and asynchronous promotion evaluation (event-driven for campaign changes).
- Run the new service in **shadow mode** for 4–6 weeks: every pricing request is sent to both the monolith and the new service; a comparator flags discrepancies.
- Only after the discrepancy rate drops below 0.01 % over two full weeks (including a weekend) begin traffic shifting via feature flags.
- Migrate pricing tables via CDC; keep monolith pricing logic compilable and deployable as rollback for 90 days.
- Assign dedicated on-call coverage for the first 30 days post-cutover.
    15. Extract Cart, Checkout, and Payment Service (Wave 5) (depends on: 14)
       Separate the **revenue-critical checkout flow** into its own service with hardened payment integration.

- Build a `checkout-service` owning cart state, checkout orchestration, and integration with the three payment providers.
- Cart state moves to a dedicated data store (Redis for transient cart, PostgreSQL for persisted orders) with CDC from the monolith during transition.
- Payment-provider integrations are wrapped in an adapter layer with circuit breakers and idempotency keys; failover order between providers is configurable per country.
- Migrate in sub-phases: (a) cart operations, (b) checkout orchestration, (c) payment capture and confirmation.
- Run chaos-engineering tests (payment-provider timeout, partial failure) before enabling real traffic.
- Rollback: feature flag routes checkout back to monolith; in-flight transactions are drained gracefully.
    16. Extract Order Management and Returns Service (Wave 6) (depends on: 15)
       Move **post-purchase order lifecycle and returns processing** into a dedicated service.

- Build an `order-service` consuming `order-placed` events from checkout; it owns order state machine, fulfilment tracking, and returns workflow.
- Integrate with the inventory service for reservation release and with the warehouse adapter for shipping updates.
- Back-office order views call the new service API through the gateway; legacy views remain as fallback.
- Migrate order and returns tables via CDC; reconcile daily during the 60-day dual-run window.
- Validate that the returns process (including cross-border returns across the 8 countries) works identically.
- Rollback re-routes order queries to the monolith; event replay ensures no order is lost.
    17. Extract Back-Office and Admin Portal (Wave 7) (depends on: 16)
       Deliver a **modern back-office** for the 300 staff users, consuming the new service APIs.

- Build a new back-office frontend (React or Vue SPA) backed by a thin BFF (Backend-for-Frontend) that aggregates calls to catalogue, pricing, order, inventory, and customer services.
- Migrate back-office routes incrementally via the gateway; legacy server-rendered admin pages remain accessible.
- Implement role-based access control (RBAC) and audit logging as cross-cutting concerns in the BFF.
- Run parallel operation for 4 weeks: staff use the new portal with a feedback channel; legacy portal stays one click away.
- Decommission legacy admin screens only after 30 days of zero critical issues.
- Provide training sessions and documentation for all 300 back-office users.
    18. Storefront Modernisation and Mobile-App API Alignment (depends on: 11, 14, 15)
       Update the **customer-facing storefront and mobile-app integration** to consume the new service layer.

- Refactor the server-rendered storefront to call service APIs via the gateway instead of hitting monolith endpoints directly.
- Introduce a Storefront BFF that aggregates catalogue, pricing, cart, and customer data for page rendering.
- Ensure the mobile app switches to the new API version behind the gateway; enforce backward compatibility for two app-release cycles.
- Implement edge caching (CDN + Varnish) for catalogue and search responses to protect services during 12× peaks.
- Validate all 4 language / 3 currency combinations through automated E2E tests.
- Rollback: gateway routes storefront traffic back to the monolith rendering path.
    19. Peak-Season Load Testing and Resilience Validation (depends on: 9, 15, 16)
       Prove the platform sustains **12× peak load** before the January and July sales windows.

- Build a load-test suite (Gatling or k6) replicating the full user journey across all 8 countries, including promo-code-heavy scenarios.
- Execute a full 12× load test in a staging environment that mirrors production topology, data volume (1.2 TB replica), and service versions.
- Run chaos-engineering experiments: kill random pods, introduce network latency, take a payment provider offline, simulate Kafka broker loss.
- Validate autoscaling: confirm that pod counts scale to handle peak within 90 seconds and scale down gracefully.
- Produce a signed-off capacity report; any component that fails the 12× test blocks go-live.
- Schedule this step at least 3 weeks before each peak season (early December for January sales, early June for July sales).
    20. Monolith Decommission and Final Data Migration (depends on: 16, 17, 18, 19)
       Retire the legacy monolith **only after all traffic is served by the new services**.

- Verify that zero production requests route to the monolith for 30 consecutive days.
- Perform a final data reconciliation: compare monolith DB checksums against service-owned databases.
- Archive the monolith codebase and database; retain read-only access for 12 months for audit and compliance.
- Decommission monolith infrastructure; reclaim compute and storage resources.
- Update all runbooks, on-call rotations, and disaster-recovery plans to reference the new service topology.
- Conduct a cross-team retrospective documenting lessons learned, technical debt incurred, and future improvement backlog.

Please, considering the previous proposals as ideas that could be considered, focus on the main objective and generate an IMPROVED proposal or a completely DIFFERENT perspective if you deem it appropriate. Only if you consider any of them is amazing and impossible to improve, answer with the same proposal.

The plan has these parts:
- "steps": the list of steps, each with "step_id" (a short label unique within this proposal, such as S1, S2, S3), "title" (a short, descriptive title), "description" (what the step does and how, in Markdown: a short opening paragraph and then, if there are several concrete things to say, a bullet list) and "dependencies" (the step_ids of the other steps of THIS proposal that must be completed first; empty if none).
- "estimated_complexity": "low", "medium" or "high".
- "success_metrics": clear and measurable success metrics, one per line as a bullet list.

Think deeply about the problem, considering all aspects and implications. Develop a comprehensive understanding of what needs to be done to achieve the objective, then produce the plan.

{"steps": [{"step_id": "S1", "title": "Charter, peak calendar and non-negotiables", "description": "Write a short **migration charter** that product, ops, finance, warehouse, payments and all five teams sign. Feature work never stops. Only production risk is constrained.\n\n- Name one accountable programme lead, a chief architect, and a weekly steering forum with a recorded risk register.\n- Ban big-bang rewrites, shared-database-first splits, unrestricted dual-writes, and irreversible cutovers.\n- Require a rehearsed rollback for every production step, with named rollback authority.\n- Publish the 12-month calendar in week one. Protect January and July with a freeze on first-time cutovers, schema splits, payment changes and traffic experiments for four weeks before each sale and two weeks after.\n- Freeze means no new migration risk, not a feature freeze. Ops has veto on search, stock, checkout and payments.", "dependencies": []}, {"step_id": "S2", "title": "Baseline the live system and business invariants", "description": "Measure the current estate before changing it. The baseline is the capacity, correctness and rollback reference for every later wave.\n\n- Trace storefront, mobile, back-office, warehouse files, payment webhooks and batch jobs onto modules, the 350 tables, stored procedures and external systems.\n- Record p50/p95/p99, error rates, conversion, payment approval, Lucene rebuild time, 15-minute inventory lag and 12x peak headroom.\n- Classify tables and procedures by writer, readers, sensitivity, retention and cross-module coupling.\n- Capture invariants: stock reservation, price and tax, promotion stacking, payment-to-order match, refunds, loyalty and GDPR deletion.\n- Produce a coupling heat map and an extraction scorecard. Keep a production-like anonymised dataset for repeatable tests.", "dependencies": ["S1"]}, {"step_id": "S3", "title": "Target architecture and honest 12-month scope", "description": "Agree a pragmatic target. Independently deployable services are the goal. Full monolith retirement is not a 12-month promise.\n\n- Bounded contexts: edge/storefront, catalogue, search, pricing and promotions, cart, checkout, payments, orders, inventory, customers and loyalty, returns, back-office.\n- One system of record per entity. Consumers may replicate data. They must not write another service’s database.\n- Prohibit distributed transactions. Use outbox, idempotent consumers, compensation, reconciliation and business exception queues.\n- Keep the monolith on Java 8. Start new services on a current LTS behind stable interfaces.\n- 12-month done means named services can deploy alone, with SLOs and rollback. Pricing engine, checkout write path and core OMS may still delegate to the monolith if parity is not proven.", "dependencies": ["S2"]}, {"step_id": "S4", "title": "Team model that keeps features flowing", "description": "Keep five domain teams. Stop treating the repository as one ownership blob. Migration is a percentage of each sprint, not a freeze.\n\n- Reserve capacity per team: about 50% business delivery, 30% migration, 20% quality and operational work. Only steering may rebalance.\n- Assign one future service owner per team plus a thin platform pair for gateway, flags, events, CI and data tooling.\n- Add code owners and module walls in the current repo first. Split repositories only after CI already works that way.\n- Product still plans features. New behaviour ships behind flags so deploy is decoupled from release.", "dependencies": ["S1", "S3"]}, {"step_id": "S5", "title": "Observability and error budgets on the monolith", "description": "Instrument the monolith as if it were already many services. You cannot extract what you cannot see.\n\n- Add structured logs, RED metrics, distributed tracing and correlation IDs across web, mobile and back-office calls.\n- Define SLOs for search, PDP, cart, checkout, payments, order create, warehouse export and back-office.\n- Page on **error-budget burn** and business failures, not only on CPU.\n- Build side-by-side dashboards for monolith versus candidate service on every cutover.\n- Add immutable audit events for price changes, payments, stock adjustments and admin actions.", "dependencies": ["S2"]}, {"step_id": "S6", "title": "Flags, CI and progressive delivery paved road", "description": "Give every team a safe way to ship without the 30-minute maintenance window. New work deploys behind flags. Old work stays on the two-week train until extracted.\n\n- Standard service template: health, readiness, graceful shutdown, telemetry, auth, config, migrations and outbox.\n- Feature flags, weighted routing, country/cohort targeting and instant revert at the edge.\n- CI with contract, characterisation and smoke tests, image scanning and automated rollback on SLO breach.\n- Preview environments that replay production-like traffic. Secrets, identities and GDPR controls are central.\n- Prove online, backward-compatible monolith deploys so routine compatible releases no longer need a maintenance window.", "dependencies": ["S3", "S4"]}, {"step_id": "S7", "title": "Safety net: journeys, contracts and 12x load", "description": "Unit coverage at 25% is not a net. Protect behaviour on the seams you will cut.\n\n- Record golden journeys for browse, price, cart, checkout, payment success and failure, order, return, loyalty and back-office.\n- Add consumer-driven contracts on every mobile and storefront endpoint. Do not force a mobile app release to extract a backend.\n- Capture characterisation tests around stored procedures and pricing before moving them.\n- Automate load, soak, spike and failover tests using the observed 12x sale profile. Run them before each sale and each major traffic expansion.", "dependencies": ["S2", "S5", "S6"]}, {"step_id": "S8", "title": "Modularise the monolith in place", "description": "Create seams before you create processes. New features may not add cross-module joins or new stored-procedure coupling.\n\n- Split packages by bounded context with compile-time architecture tests.\n- Replace in-process calls at boundaries with interfaces. Branch by abstraction.\n- Wrap pricing, checkout and inventory access behind facades even while they still run in-process.\n- Use expand-contract schema changes only. Additive first. Destructive later, with evidence all readers moved.\n- Raise regression coverage on any module before it is touched.", "dependencies": ["S3", "S7"]}, {"step_id": "S9", "title": "Strangler edge with instant traffic rollback", "description": "Put a reverse proxy in front of every public and mobile endpoint. Clients keep the same URLs. You choose monolith or service per route and percentage.\n\n- Preserve headers, sessions, cookies, the four languages, three currencies and eight countries.\n- Route by path, country, cohort, flag and percentage. Default remains the monolith.\n- Shadow traffic before any live percentage. Measure equivalence and gateway latency overhead first.\n- Rollback is a **route change**, not a redeploy, and must complete in minutes including in-flight requests.\n- Storefront SSR and the mobile app stay compatible until a later BFF if needed.", "dependencies": ["S5", "S6", "S7"]}, {"step_id": "S10", "title": "Events, outbox, CDC and reconciliation spine", "description": "Give the monolith a reversible integration spine. Services subscribe to facts. They do not call each other’s databases.\n\n- Transactional outbox in the same Postgres transaction as business writes. CDC only where an outbox cannot yet be added, with a time-bound replacement plan.\n- Versioned events for product, price, stock, customer, order and return. Schema registry, idempotent consumers, dead letters and replay.\n- A reconciliation product: counts, hashes, money totals, stock totals, lag and exception queues.\n- Entity transition states: monolith-owned, replicated read, dual-read validation, service-owned with compatibility adapter, then legacy-retired.\n- During any trial, one command owner writes. The monolith write wins on conflict until ownership is deliberately transferred.", "dependencies": ["S5", "S8"]}, {"step_id": "S11", "title": "Extract search as the first service", "description": "Replace the nightly Lucene rebuild with an independently deployed search service. This is read-heavy, already eventually consistent, and off the payment path.\n\n- Index from catalogue and related events, not from a nightly dump. Support incremental updates, aliases and blue/green indexes.\n- Shadow queries against current Lucene until precision, recall, facets, zero-results and latency match.\n- Shift traffic 1% → country cohort → 10% → 50% → 100% with instant route rollback.\n- Keep the old index warm through the next sale as standby. Search must not become authoritative for price or stock.", "dependencies": ["S9", "S10"]}, {"step_id": "S12", "title": "Extract catalogue read models", "description": "Serve product, media and localisation from a catalogue service. Writes can stay in the monolith until merchandising has a new path.\n\n- Build country and language read models for eight markets around one product identity.\n- Feed from monolith-owned data via outbox or controlled replication. Stop new cross-module catalogue joins.\n- Cut storefront and mobile read traffic via the strangler after shadow comparison.\n- Cache with explicit stale limits and a bypass control. Do not move authoring tools until reads are boring.", "dependencies": ["S11"]}, {"step_id": "S13", "title": "Inventory adapter and availability reads", "description": "Separate warehouse file exchange from customer-facing availability. Keep the warehouse contract unchanged.\n\n- Adapter validates, deduplicates and acknowledges inbound and outbound files. Publish inventory-change events from that adapter.\n- Availability read model for storefront and search, with freshness targets and oversell tolerance made explicit.\n- Shadow-compare every SKU and warehouse against the monolith. Reconcile before any traffic shift.\n- Leave reservation and allocation authority in the monolith until order ownership is designed.\n- Immediate fallback to monolith availability and a replayable file-recovery path. Prove no extra oversell versus today’s 15-minute lag before a sale.", "dependencies": ["S9", "S10"]}, {"step_id": "S14", "title": "Customer, session and loyalty with GDPR", "description": "Move identity-adjacent data only after consent, retention and deletion are clear. Avoid inconsistent account state across countries and channels.\n\n- Start with a replicated profile read service. Then migrate bounded profile writes through a façade with idempotency and audit.\n- Migrate sessions without forced logouts. Web and mobile keep current cookies or tokens during the switch.\n- Loyalty in slices: balance inquiry before accrual or redemption, with a ledger and daily reconciliation.\n- Subject-access and deletion must work in both systems. Rollback restores monolith auth with no password resets.", "dependencies": ["S9", "S10"]}, {"step_id": "S15", "title": "Pricing archaeology, golden masters and façade", "description": "Do not rewrite the 200,000-line pricing module from tribal knowledge. Tests become the spec.\n\n- Cross-functional squad: engineers, merchandising, finance, country ops and QA.\n- Inventory rules, stored procedures, config tables, overrides, jobs and manual back-office actions.\n- Capture production decision traces for eight countries and three currencies into a privacy-safe golden-master corpus.\n- Put the existing engine behind a versioned pricing façade. New callers use the façade even while it delegates to monolith logic.\n- New promo features implement against the façade. Keep the legacy engine deployable as rollback through the next two sales.", "dependencies": ["S2", "S7", "S8"]}, {"step_id": "S16", "title": "Dual-run only proven pricing slices", "description": "Run a candidate pricing service in shadow until it matches the monolith on live baskets. Checkout keeps monolith prices until the money path is clean.\n\n- Extract only well-understood rule slices. Compare exact price, tax, discount, explanation and latency.\n- Alert on any mismatch. Require business sign-off and financial-impact classification before live routing.\n- Shift read traffic first, then promo-usage writes, country by country if needed.\n- If full engine extraction is not safe inside 12 months, the independently deployable artefact is the façade plus proven slices. That is success, not failure.", "dependencies": ["S10", "S12", "S15"]}, {"step_id": "S17", "title": "Order query, notifications and returns slices", "description": "Create independently deployable order value without splitting the transactional checkout path yet.\n\n- Publish reliable order lifecycle events from the monolith outbox.\n- Order query service for self-service, customer service and selected back-office views, with freshness labels and monolith fallback.\n- Extract bounded workflows such as notifications, return initiation and return-status tracking where ownership is explicit.\n- Preserve order creation, capture, cancel, refund authority and warehouse export in the monolith until S20.\n- Reconcile counts, states, refunds, returns and event lag continuously.", "dependencies": ["S10", "S14"]}, {"step_id": "S18", "title": "Checkout façade and payment adapters", "description": "Strangle checkout without rewriting the three payment providers. A thin orchestration layer talks to existing integrations first.\n\n- Define cart identity, guest merge, session persistence, promotion snapshots, inventory checks and checkout idempotency keys.\n- Checkout façade initially delegates to the monolith. Route web and mobile gradually with response compatibility.\n- Isolate each provider behind versioned adapters: tokens, webhook verification, idempotent auth/capture, retries, ledger and settlement reconciliation.\n- Canary by country and payment method. In-flight payments complete on the old path if you roll back.\n- Do not split final order-creation until failure modes, compensation, support procedures and 12x tests show acceptable risk.", "dependencies": ["S12", "S13", "S16", "S17"]}, {"step_id": "S19", "title": "Independent pipelines after the first service is real", "description": "When a service is independently releasable, stop bundling it into the fortnightly artefact. The remaining monolith keeps the old train until it is small.\n\n- One pipeline per service: test, canary, promote, revert. Contract tests gate consumer and provider deploys.\n- Split repos only after module walls and CI already work in the monorepo.\n- Target at least weekly independent releases, then daily where risk is low.\n- Each service has named owners, on-call, runbooks, SLOs and a practised rollback.", "dependencies": ["S6", "S11"]}, {"step_id": "S20", "title": "Single-writer ownership cutovers", "description": "Move write ownership one entity group at a time after read parity and operations are boring. Each cutover is a reversible state transition, not a one-time database move.\n\n- Document source of truth, writer sequence, replication direction, consumers, retention, reconciliation and rollback point.\n- Backfill with checksums. Dual-read validate. Then switch the single writer. Avoid unrestricted dual-writes.\n- Halt traffic expansion automatically on reconciliation or SLO thresholds.\n- Schedule high-risk ownership moves outside sales protection windows, with a rollback rehearsal and staffed hypercare.\n- Stored procedures leave only when the characterisation harness has an equivalent in service code.", "dependencies": ["S10", "S11", "S12", "S13", "S14", "S16", "S17", "S18"]}, {"step_id": "S21", "title": "First peak-season certification", "description": "Certify the hybrid estate and every fallback before the first of January or July, whichever comes first. A service is not ready if its rollback target cannot take the traffic.\n\n- Freeze new cutovers in the protection window. Feature work may continue behind flags.\n- Load-test the live routing ratio at least 12x observed baseline plus headroom, including gateway, caches, monolith, services, events, search, payments and warehouse files.\n- Prove traffic reversion to the monolith and that Java 8 plus 1.2 TB Postgres can absorb reverted load.\n- Game days: provider timeout, CDC lag, flag revert, search fallback, stock file delay.\n- Formal go/no-go from engineering, ops, commerce, finance, warehouse and support.", "dependencies": ["S7", "S9", "S11", "S13"]}, {"step_id": "S22", "title": "Second peak-season certification", "description": "Repeat certification before the second sale with more services in the path. Do not enter the window with unproven checkout, payment or pricing traffic shifts.\n\n- Re-run 12x hybrid load and rollback-to-monolith tests on the then-current topology.\n- Confirm price-parity, payment approval, order throughput and inventory discrepancy stay within agreed thresholds.\n- Warm caches, pre-scale, agree provider rate limits and staff a war room.\n- After the sale, compare actuals to forecasts and freeze lessons into the next wave.", "dependencies": ["S16", "S18", "S21"]}, {"step_id": "S23", "title": "Shrink the monolith and hand over governance", "description": "Remove only proven-obsolete paths. Retain legacy where removal creates unjustified commercial risk.\n\n- Decommission endpoints, jobs, Lucene, tables and procedures only after consumer inventory, archival, reconciliation and rollback-retention periods.\n- Measure residual coupling, direct DB access and operational toil. Fund leftover pricing, checkout or OMS decomposition as a follow-on roadmap.\n- Quarterly architecture review, API and event lifecycle, service scorecards and DR exercises.\n- End state of this year: independently deployable services, instant rollback still in place, no unplanned downtime attributed to the programme, and a smaller but still honest monolith if that is what risk demanded.", "dependencies": ["S19", "S20", "S22"]}], "estimated_complexity": "high", "success_metrics": "- Zero unplanned customer-facing downtime attributed to migration work in the 12 months.\n- Every production step has a rehearsed rollback that restores the previous path in minutes without breaking payments, orders or stock integrity.\n- January and July sales complete at or above pre-migration peak availability, conversion, payment approval and order throughput, including 12x headroom plus agreed reserve.\n- Feature throughput stays at least 80% of the agreed baseline. No programme-wide feature freeze.\n- Search, catalogue reads, inventory availability, customer/loyalty slices, order query/returns slices, pricing façade (and any proven rule slices), and checkout/payment façade are independently deployable with owners, SLOs, dashboards and on-call.\n- Dual-run mismatch on price and stock is below the agreed threshold before each traffic shift, with a target of zero unresolved differences on money paths.\n- For each migrated entity group, unresolved record discrepancies stay under 0.01% and unresolved financial discrepancies stay at zero at cutover completion.\n- No new cross-context joins. Extracted domains make zero stored-procedure calls after ownership transfer. No service writes another service’s database.\n- Mean time to revert a bad service release is under 10 minutes via flags or routing. Critical journey detect time is under 5 minutes.\n- Mobile and storefront keep compatible endpoints throughout. Warehouse file contracts remain valid until the warehouse side can change.\n- Deployment frequency for extracted services reaches at least weekly, with no mandatory 30-minute maintenance window for routine compatible releases."}

[SYSTEM]
You are an expert assistant in complex project planning.
Your task is to generate a detailed and structured action plan to reach the main objective in the most professional and most detailed way, no matter how much work or steps will be needed to perform.
Use your internal reasoning processes to think deeply about the problem and create the most comprehensive plan possible.
Take as much time and space as you need to think through all aspects of the problem.
After your thorough analysis, answer with the plan in the requested structure.

Every text field you write will be read by a busy person, so write for them: short sentences, one idea each, in short paragraphs. Text fields accept Markdown: separate paragraphs with a blank line, use a bullet list when you enumerate things and plain paragraphs when you explain or argue, and bold at most one key phrase per paragraph. A text of more than three sentences must be split into paragraphs; never deliver a single unbroken block of text.

[HUMAN]
Main Objective: "A mid-size European fashion retailer runs its e-commerce on a 10-year-old monolith: Java 8 / Spring, about 2 million lines, one PostgreSQL database of 1.2 TB with 350 tables and heavy use of stored procedures and cross-module joins. It serves 8 countries, 3 currencies and 4 languages, with about 40,000 orders a day and peaks of 12x during sales. The monolith contains: the storefront (server-rendered, plus a separate mobile app hitting the same endpoints), catalogue and search (a Lucene index rebuilt nightly), pricing and promotions (the most complex module, 200,000 lines, with country-specific rules nobody fully understands), cart and checkout with three payment providers, order management, inventory synchronised every 15 minutes with the warehouse system via file exchange, customer accounts and loyalty, returns, and a back-office used by 300 staff. Deployments happen every two weeks as a single artefact with a 30-minute maintenance window; test coverage is 25% and mostly unit tests. Five teams of 8 developers, each organised around a business area but all committing to the same repository.

Plan the migration to independently deployable services over 12 months with no feature freeze, no unplanned downtime and the ability to roll back every step, preserving the peak-season capacity (sales in January and July must not be put at risk)."


For your consideration and refinement, here are proposals from the previous round:

  Previous Proposal 1 (ID: 655fdafb-56dd-4ad6-bd0f-4bb657813959, Agent: claudeHaiku4.5_initial_1, LLM: anthropic/claude-haiku-4-5):
    Estimated Complexity: high
    Success Metrics: - All major business functions (catalog, pricing, orders, payments, inventory) deployed as independently releasable services by end of month 12.
- Deployment frequency increased from bi-weekly to daily or on-demand per team, with zero coordination required between teams.
- Peak-season capacity maintained: handle 40,000 orders/day baseline and 480,000 orders/day (12x peak) with P95 page load time ≤2 seconds and payment success rate >99.9%.
- Zero unplanned downtime caused by the migration during the 12-month period; any incidents are handled with automated rollback within 5 minutes.
- Test coverage increased from 25% to ≥70% across all services, with comprehensive integration test suite running on every commit.
- Mean time to recovery (MTTR) for production incidents reduced from hours to <15 minutes due to circuit breakers and fallbacks.
- Data consistency validated: automatic nightly checks confirm service data matches source-of-truth, with any discrepancies logged and investigated within 24 hours.
- Service API latency (p95) meets SLOs: catalog ≤200ms, pricing ≤300ms, inventory ≤200ms, payment ≤1000ms, with circuit breakers preventing cascading failures.
- Each service has documented runbooks, incident response procedures, and scaling playbooks; all ops and dev teams trained and confident.
- Feature delivery velocity maintained at pre-migration levels: new feature deployment time remains unchanged despite architectural changes.
    Steps (23):
    1. Establish governance and migration steering committee
       Create a governance structure to guide the 12-month migration and ensure alignment across teams.

- Define clear decision-making authority and escalation paths.
- Establish weekly steering meetings with representatives from each of the five teams plus leadership.
- Create a shared vision for service boundaries and prioritize which modules to extract first.
- Set up RACI matrix (responsible, accountable, consulted, informed) for each major service extraction.
    2. Design service architecture and system boundaries (depends on: 1)
       Map the monolith into independently deployable services with clear boundaries and synchronization points.

- Analyze the 350 tables and identify which tables belong to each business domain (catalog, pricing, orders, inventory, etc.).
- Design the data synchronization strategy for the 1.2 TB database, including which data moves to which service.
- Plan the strangler approach for each module: what gets extracted first, what depends on what.
- Define API contracts and asynchronous messaging patterns (events vs. direct calls) between services.
    3. Deploy Kubernetes infrastructure and container registry (depends on: 2)
       Build the cloud infrastructure to run containerized services at scale with redundancy and monitoring.

- Provision a production-grade Kubernetes cluster (managed service like EKS, AKS, or on-premises).
- Set up container image registry with retention policies and security scanning.
- Configure persistent storage volumes for databases and caches.
- Implement cluster networking, RBAC, and network policies for security.
    4. Implement strangler proxy and API gateway (depends on: 3)
       Deploy a reverse proxy that routes requests between the monolith and the new services, enabling gradual traffic migration.

- Deploy API gateway (e.g., Kong, Ambassador, or cloud-native option) in front of the monolith.
- Implement request routing logic to direct traffic to services or monolith based on rules.
- Add request/response logging and transformation layer for API versioning.
- Enable instant traffic rerouting and rollback if a service fails.
    5. Build feature flagging and traffic routing system (depends on: 3)
       Implement a system to control which users hit which service, enabling safe canary deployments and A/B testing.

- Choose or build a feature flag platform (LaunchDarkly, Unleash, or open source).
- Integrate with API gateway and service code to support percentage-based traffic splitting.
- Enable per-user, per-region, and per-browser routing for testing (e.g., mobile app to new service before web).
- Create dashboards for ops teams to adjust routing in real time without redeploying.
    6. Establish comprehensive observability (logs, metrics, tracing) (depends on: 3)
       Deploy centralized logging, metrics, and tracing to track behavior across services and detect issues early.

- Set up centralized log aggregation (e.g., ELK, Splunk, or cloud-native solution).
- Deploy metrics collection (Prometheus, Datadog, or equivalent) with dashboards for each service.
- Implement distributed tracing (Jaeger, Zipkin) to track requests across service boundaries.
- Define critical alerts: error rates, latency spikes, database query performance, payment transaction failures.
    7. Design event-driven data consistency architecture (depends on: 3)
       Plan how services will stay in sync when sharing data extracted from the monolith's single database.

- Design an event bus or message queue topology (Kafka, RabbitMQ, or cloud equivalent).
- Plan Change Data Capture (CDC) from the monolith to notify services when data changes.
- Define saga patterns for multi-step distributed transactions (e.g., order creation spanning multiple services).
- Document how to handle eventual consistency, conflicts, and zombie data in each service.
    8. Build inter-service communication framework (APIs and queues) (depends on: 3)
       Establish libraries and standards for how services talk to each other synchronously and asynchronously.

- Define REST or gRPC standards (authentication, versioning, error handling) for all service-to-service calls.
- Create shared libraries for message publishing/consuming (idempotency, dead-letter handling).
- Document timeout and retry policies to prevent cascading failures.
- Provide templates and SDKs to development teams so they don't reimplement these patterns.
    9. Extract catalog and search service (depends on: 4, 5, 6, 8)
       Extract the catalog and Lucene search index into its own service, starting with a low-risk module to validate the pattern.

- Move catalog module code from monolith to a new service repository.
- Containerize the service and deploy to Kubernetes.
- Keep the existing Lucene index and nightly rebuild process initially.
- Route catalog API requests through the gateway: send 10% of traffic to new service first, validate results, increase to 100%.
    10. Create independent catalog data layer with synchronization (depends on: 9, 7)
       Extract catalog tables from the shared database and sync changes from the monolith to the new service.

- Copy catalog tables to a new PostgreSQL database managed by the catalog service.
- Implement CDC (Change Data Capture) to publish catalog changes as events when the monolith updates data.
- Build catalog service to subscribe to these events and update its own tables.
- Implement consistency checks: run hourly validation that catalog service data matches monolith source-of-truth, log discrepancies.
    11. Extract customer accounts service (depends on: 4, 5, 6, 8)
       Move customer profile, login, and loyalty data into a dedicated service that other services query.

- Extract customer and loyalty tables from monolith database.
- Build service to manage customer profile, authentication, and loyalty points.
- Implement event stream for customer changes (profile updates, loyalty point transactions).
- Route customer API calls through gateway; monolith and new service share database briefly, then switch to CDC sync.
    12. Extract returns management service (depends on: 4, 5, 6, 8)
       Create a focused returns processing service to further validate the extraction pattern and learn before tackling complex modules.

- Move returns processing logic and tables from monolith.
- Build simple service with clear inputs (return requests) and outputs (refund events).
- Connect to order data via API calls (will be extracted separately) and inventory service.
- Canary traffic, monitor error rates and latency; this is the lowest-risk extraction.
    13. Audit, document, and decompose pricing/promotions business rules (depends on: 1)
       Reverse-engineer and document the complex pricing logic to enable rebuilding it as a new service. Start early in parallel with infrastructure work.

- Form a task force: architects, the original pricing team, and business analysts.
- Read through the 200k lines of pricing code; document country-specific rules, exceptions, and dependencies (which rules call which).
- Build a comprehensive spreadsheet of pricing scenarios: free shipping rules, discount types, country-specific taxes, dynamic pricing, etc.
- Extract test cases from production data: get 1,000 real orders from each country and document how pricing rules applied.
- Identify which pricing decisions depend on cart, inventory, or customer account data.
    14. Design and implement pricing/promotions service with enhanced testing (depends on: 4, 5, 6, 8, 13)
       Rebuild the pricing logic as a new microservice with a cleaner architecture and comprehensive test coverage.

- Architect the new service with clear separation: promotion evaluation, tax calculation, discount application, price transformation per country.
- Implement each country's rules as either code or a rules engine (not hardcoded strings).
- Build unit tests for 100+ pricing scenarios (cross-reference with S13 test cases).
- Implement shadow traffic testing: send real production requests to both monolith and new service, log differences, investigate discrepancies before switching traffic.
    15. Implement event-driven pricing and cart synchronization (depends on: 14, 7, 9)
       Sync pricing changes and promotions between the pricing service and cart/checkout to keep pricing consistent in real time.

- Publish events when promotions are created/updated: promotion_created, promotion_updated, promotion_ended.
- Implement cart service subscription: when a cart is modified or promotion changes, recalculate cart total.
- Handle time-based promotions: if a promotion starts/ends during a customer's shopping, reflect immediately.
- Validate consistency: sample 1% of checkouts, compare price calculated by pricing service vs. what customer paid; alert if mismatch.
    16. Extract inventory management service (depends on: 4, 5, 6, 8, 10)
       Create a service that manages stock levels and warehouse synchronization, replacing the 15-minute batch sync with event-driven updates.

- Extract inventory tables and warehouse sync logic from monolith.
- Build inventory service that subscribes to warehouse file drops (replace file exchange with event publishing or direct API).
- Implement real-time inventory updates: when an order is placed, reserve stock immediately; when warehouse sends stock count, update available qty.
- Canary deploy and validate: monitor for stock mismatch errors (overselling); maintain monolith as source-of-truth with service as secondary initially.
    17. Extract payment gateway coordination service (depends on: 4, 5, 6, 8)
       Abstract the three payment providers into a dedicated service so checkout doesn't depend on external API details.

- Move payment provider logic (Stripe, PayPal, local provider) from monolith checkout to new service.
- Implement payment orchestration: route to correct provider based on country/currency, handle failures, retry logic.
- Build payment event stream: payment_initiated, payment_authorized, payment_captured, payment_failed, payment_refunded.
- Test thoroughly: use sandbox accounts, simulate failure scenarios (provider timeout, decline, network error); ensure consistent error messages to checkout.
- Use gateway to route: send payments for test users/regions to new service first.
    18. Implement resilience patterns across services (circuit breakers, fallbacks, retries) (depends on: 9, 10, 11, 12)
       Make services robust to failures of dependent services; services should handle failures gracefully, not crash the whole system.

- Install circuit breaker library (Resilience4j, Hystrix equivalent) in each service.
- Define circuit breaker policies per dependency: if catalog service is slow, circuit opens after 50 failures or 5 seconds slow response, fails fast.
- Implement fallback strategies: if pricing service is down, use cached pricing; if inventory is down, temporarily increase order-to-fulfillment delay.
- Set timeouts on all cross-service calls (e.g., cart→pricing must return in 500ms) with bulkhead pattern to prevent resource exhaustion.
- Test: use chaos monkey or chaos toolkit to inject failures (kill pods, add latency) and verify fallbacks work.
    19. Build comprehensive integration test suite (depends on: 14, 16, 17)
       Create automated tests that exercise real customer journeys across multiple services to catch bugs before production.

- Build test data setup: create products, customers, promos, inventory in test environment.
- Write end-to-end test scenarios: browse catalog → add to cart → apply promo → checkout with payment → order created → inventory updated → returns processing.
- Implement performance tests: simulate 40,000 orders/day baseline load, 480,000 orders (12x peak) burst load; validate response times and error rates.
- Add chaos tests: run scenarios while services fail (pod restart, network partition, database slow) to validate resilience.
- Run tests on every service commit and nightly against staging environment; alert on test failure.
    20. Create independent service deployment pipelines (depends on: 4, 18)
       Set up automated deployment so each service can be released independently without coordinating with other teams every two weeks.

- For each service: build → run tests → build container image → push to registry → deploy to staging with canary (5% traffic initially).
- Implement automated rollback: if error rate on new service exceeds threshold for 5 minutes, automatically route traffic back to old version and alert.
- Add manual approval gates for production: team lead reviews test results, approves, release happens with 0 downtime (health checks, graceful shutdown).
- Documentation: each team has runbook for deploying their service, rolling back, handling incidents.
- Target: enable each team to deploy 1-2 times per day if needed.
    21. Conduct load testing and peak-season capacity planning (depends on: 19, 20)
       Validate that the new service architecture can handle peak loads (40k baseline, 480k at 12x peak) without degradation.

- Load test in staging: ramp up traffic gradually, measure latency, error rate, and resource usage (CPU, memory, database connections).
- Identify bottlenecks: where does latency spike first? Is it database queries, service CPU, or network?
- For each service and the database: determine max capacity and burst capacity (e.g., catalog service handles 500 QPS sustained, 1000 QPS for 30s burst).
- Plan auto-scaling: set Kubernetes horizontal pod autoscaler min/max replicas, database read replicas, and caching layers based on results.
- Validate payment processing: simulate peak payment volume with all three providers; confirm no payments are lost or duplicated.
    22. Execute comprehensive pre-peak-season validation and simulation (depends on: 21)
       Run a full-dress rehearsal before January/July peak season to ensure the system is ready; critical gate before any further changes.

- Schedule 48-hour end-to-end test: run production-like load against all services with canary deployments to catch integration issues.
- Run disaster recovery drill: if one service is down, can customers still browse and checkout? If payment provider fails, can we use fallback?
- Customer journey validation: have real team members and friendly customers test: browse → add items → apply promo code → checkout on web and mobile apps.
- Team readiness: hold incident response drill, confirm runbooks are accurate, escalation paths clear, and alert thresholds are tuned.
- Performance sign-off: confirm P95 page load times, checkout flow time, order processing latency all meet SLOs for peak traffic.
- Go/no-go decision: leadership reviews results; if any critical issue, fix and re-test before peak season starts.
    23. Monitor, optimize, and prepare for ongoing evolution (depends on: 22)
       After validation, monitor the production system closely during peak season; optimize based on real behavior and plan next improvements.

- Daily monitoring during peak season: dashboard watching error rates, latency, payment success rate, customer support tickets.
- Real-time tuning: if one service is bottleneck, increase replicas or add caching; if database query is slow, add index (non-blocking).
- Post-peak analysis: compare actual peak performance to projections; document what assumptions were wrong, what worked well.
- Identify quick wins: which cross-service calls can be cached, which synchronous calls could be async, which services could be split further?
- Plan the next 6 months: which remaining monolith modules can be extracted, which services need optimization, should we change tech stack for any?

  Previous Proposal 2 (ID: 5f35320e-40bf-4797-a282-13ff94f785f1, Agent: gpt-5.6-terra_initial_2, LLM: openai/gpt-5.6-terra):
    Estimated Complexity: high
    Success Metrics: - No unplanned customer-facing downtime is attributable to migration work during the 12-month programme.
- Every production migration has a documented, rehearsed rollback that can be initiated within 15 minutes and preserves financial, order, payment, and inventory integrity.
- January and July sales periods complete with at least the pre-migration peak availability, conversion rate, payment approval rate, and order throughput.
- The hybrid platform sustains at least 12x observed normal load plus agreed headroom in full-path load and failover tests before each sales period.
- Critical journeys achieve at least 95% automated API, integration, contract, and end-to-end regression coverage by business-risk weighting, with 100% coverage of defined checkout, payment, order, stock, refund, and price-parity scenarios.
- Catalogue/search, inventory availability, customer/loyalty slices, order query/post-order slices, and selected checkout/payment façade capabilities are independently deployable with named ownership, SLOs, dashboards, runbooks, and on-call support.
- All extracted services have zero direct writes to another service's database, and all cross-service state propagation uses governed APIs or versioned events.
- For each migrated entity group, reconciliation identifies less than 0.01% unresolved record discrepancies and zero unresolved financial discrepancies at cutover completion.
- Pricing and promotion decision parity for any migrated rule slice is at least 99.99% against approved golden-master cases, with all remaining differences explicitly approved by business owners.
- Deployment frequency for independently deployable services reaches at least weekly, with no mandatory monolith maintenance window required for routine compatible releases.
- Mean time to detect critical customer-journey failures is below 5 minutes, and mean time to restore or roll back migration-related severity-one incidents is below 30 minutes.
- Feature delivery continues throughout the programme, with planned business roadmap throughput maintained at no less than 80% of the agreed baseline.
    Steps (20):
    1. Establish migration governance and delivery model
       Create a migration programme that protects revenue, peak periods, and ongoing feature delivery. Assign one accountable programme lead, a chief architect, and named business and operational owners for every domain.

- Create a steering group with engineering, product, operations, security, finance, warehouse, payments, and country representatives.
- Reserve capacity per team: 50% business delivery, 30% migration work, and 20% quality, operational, and unplanned-work reduction. Rebalance only through the steering group.
- Publish decision rights, architecture principles, risk register, dependency board, and weekly programme cadence.
- Define explicit stop/go criteria for each production cutover and a formal rollback authority.
- Plan sales protection windows: no first-time domain cutovers, database schema changes, payment changes, or major traffic experiments during the four weeks before and through January and July sales periods.
- Keep feature work flowing through the same delivery pipeline, with feature flags used to decouple code deployment from customer release.
    2. Baseline the monolith, traffic, data, and operational risk (depends on: 1)
       Build an evidence-based picture of the current system before selecting extraction order. The baseline becomes the capacity, correctness, and rollback reference for every migration wave.

- Map request flows from web, mobile, back-office, warehouse files, payment providers, and scheduled jobs to modules, tables, stored procedures, queues, and external dependencies.
- Measure normal and sale-peak throughput, latency, error rates, database load, index rebuild duration, batch duration, payment approval rates, and recovery times.
- Classify all 350 tables and stored procedures by owning business concept, writers, readers, sensitivity, retention requirements, and cross-module coupling.
- Identify critical business invariants, including stock reservation, price calculation, promotion eligibility, payment-to-order consistency, returns, loyalty accrual, and country tax requirements.
- Produce dependency heat maps and a candidate extraction scorecard using coupling, business risk, change frequency, data ownership feasibility, and expected value.
- Capture a production-like anonymised data set and documented peak-load profiles for repeatable testing.
    3. Define target architecture and domain boundaries (depends on: 2)
       Agree a pragmatic target architecture based on bounded contexts, clear data ownership, and incremental extraction. Do not start by redesigning every business process or splitting every table.

- Define initial bounded contexts: edge/storefront experience, catalogue, search, pricing and promotions, cart, checkout, payments, orders, inventory, customers and loyalty, returns, and back-office workflow.
- Assign a single system of record and an owning team for each business data entity. Services may consume replicated data but must not directly write another service's database.
- Define synchronous API rules, asynchronous event rules, versioning rules, idempotency requirements, correlation identifiers, and error-handling conventions.
- Establish a platform pattern: containerised services, managed or highly available PostgreSQL where appropriate, API gateway or edge routing, event transport, secrets management, central configuration, and infrastructure as code.
- Select an incremental strangler pattern. New services are introduced behind stable interfaces while the monolith remains the source of truth until ownership is deliberately transferred.
- Document explicitly that distributed transactions are prohibited. Use outbox, idempotent consumers, compensating actions, reconciliation, and business-visible exception queues instead.
    4. Create production safety foundations (depends on: 1, 3)
       Make every current and future component observable, operable, and auditable before material traffic is moved. This work starts in the monolith as well as in new services.

- Implement standard structured logs, metrics, distributed tracing, correlation IDs, service dashboards, synthetic customer journeys, and business KPIs.
- Define service-level objectives for storefront availability, search, price response, cart operations, checkout, payment confirmation, order creation, and warehouse export.
- Add alerting with severity, ownership, escalation paths, and tested runbooks. Alert on business failures as well as infrastructure failures.
- Establish immutable audit events for pricing changes, promotion decisions, payments, order state changes, stock adjustments, customer-data access, and administrative actions.
- Implement backup, restore, disaster recovery, and failover tests for the monolith database, new data stores, event platform, and search platform.
- Create a shared operations readiness review required before any service receives production traffic.
    5. Build secure delivery and runtime platform (depends on: 3, 4)
       Provide a paved road for independently deployable services. The platform must reduce deployment risk rather than create a second operational burden.

- Build standard service templates for Java, including health checks, readiness checks, graceful shutdown, telemetry, API documentation, authentication, configuration, database migrations, and outbox publishing.
- Implement CI/CD with build provenance, dependency and container scanning, automated unit, contract, integration, and smoke tests, environment promotion, and approval controls for high-risk releases.
- Provision isolated development, integration, staging, performance, and production environments through infrastructure as code.
- Introduce progressive delivery capabilities: feature flags, canary releases, blue/green deployment where justified, traffic splitting, automated rollback, and deployment freeze controls.
- Centralise secrets, certificate rotation, least-privilege service identities, encryption, vulnerability management, and GDPR data-handling controls.
- Ensure platform capacity is sized and load-tested for at least the documented 12x sales peak plus agreed headroom.
    6. Improve monolith safety while it remains live (depends on: 2, 4, 5)
       Stabilise the monolith so it can safely coexist with extracted services for most of the programme. The monolith remains a production dependency and needs the same operational discipline as new services.

- Add a modularity boundary map and enforce it with architecture tests, package rules, code ownership, and mandatory reviews for cross-module changes.
- Wrap high-risk database access behind repository or application interfaces, beginning with areas selected for extraction.
- Introduce expand-contract database migration rules. Additive, backward-compatible changes deploy first; destructive changes require evidence that all readers have moved.
- Raise automated regression coverage around critical journeys before touching them, using API, integration, and end-to-end tests rather than relying only on unit tests.
- Add feature flags and kill switches around all new monolith-to-service integrations.
- Reduce the 30-minute maintenance dependency by proving online deployment procedures, connection draining, backward-compatible schema releases, and zero-downtime smoke tests.
    7. Implement integration, event, and data-transition patterns (depends on: 3, 5, 6)
       Create reusable patterns for safe coexistence between the monolith and services. This is the core mechanism for reversible migration without dual-write corruption.

- Introduce an event backbone and schema registry or equivalent governance, with versioned events, retention policies, dead-letter handling, replay procedures, and consumer ownership.
- Implement transactional outbox publishing in the monolith and each service. Events are committed with source data and delivered asynchronously with deduplication.
- Provide change-data-capture only where an outbox cannot initially be added, with monitoring and a time-bound plan to replace it.
- Build a replication and reconciliation framework that compares source and target counts, hashes, business totals, lag, and exception records.
- Standardise anti-corruption adapters so services do not inherit monolith-specific data shapes and semantics.
- Define transition states for each entity: monolith-owned, replicated read model, dual-read validation, service-owned with monolith compatibility adapter, and legacy-retired.
    8. Create quality, performance, and release assurance (depends on: 2, 4, 5, 7)
       Replace confidence based on a fortnightly monolith release with automated evidence for each independently deployed component. Focus first on revenue-critical and migration-affected flows.

- Build a production-like test environment with anonymised data, external-provider simulators, warehouse-file simulators, and repeatable country, currency, language, tax, and promotion test fixtures.
- Establish consumer-driven API and event contract tests. Producers may not release breaking changes until consumers have migrated or compatibility periods expire.
- Create end-to-end tests for browse-to-order, guest and registered checkout, payment success and failure, cancellation, return, refund, stock changes, loyalty, and back-office operations.
- Implement load, soak, spike, chaos, and failover tests using the observed 12x sale profile. Run them before every traffic expansion.
- Use shadow execution for high-risk decisions. Compare service and monolith outputs without changing customer outcomes.
- Set release gates for security, contracts, performance, observability, rollback rehearsal, and business reconciliation.
    9. Select and sequence extraction waves (depends on: 2, 3, 8)
       Prioritise small, low-coupling seams first, then use the resulting capabilities for harder domains. Pricing, promotions, checkout, and core order ownership are deliberately not first-wave candidates.

- Wave 1: edge routing, read-only catalogue API, search, and selected back-office read/reporting capabilities.
- Wave 2: inventory availability read model and warehouse integration adapter, while preserving the current order and stock authority initially.
- Wave 3: customer profile and selected loyalty read/write capabilities, subject to GDPR and identity constraints.
- Wave 4: order query model, notification or non-core order workflow, and returns workflow where process boundaries are confirmed.
- Wave 5: cart and checkout façade components, followed by payment-provider adapters only after reliability evidence is sufficient.
- Treat pricing and promotions as a dedicated discovery-and-modernisation stream. Extract only verified, bounded slices after exhaustive parity testing; retain the monolith engine behind an API if full extraction is not safe within 12 months.
- Define per-wave entry criteria, exit criteria, capacity allocation, and a no-go rule for work that would cross a sales protection window.
    10. Introduce edge routing and façade interfaces (depends on: 4, 5, 6, 8)
       Decouple channels from monolith internals before extracting business capabilities. Web, mobile, and back-office clients must use stable, versioned interfaces rather than service-specific implementation details.

- Place an API gateway or backend-for-frontend layer in front of existing endpoints without changing functional behaviour.
- Route by path, tenant/country, customer cohort, feature flag, and percentage of traffic. Default routing remains to the monolith.
- Add authentication, authorisation, rate limiting, request validation, response compatibility, correlation IDs, and traffic dashboards at the edge.
- Preserve mobile API compatibility through versioning and adapter endpoints. Do not force a mobile release as a prerequisite for backend extraction.
- Implement instant route rollback to the monolith, including tested handling for sessions, carts, cached responses, and in-flight requests.
- Measure baseline response equivalence and latency overhead before moving any business endpoint.
    11. Extract catalogue read API and modern search (depends on: 7, 8, 9, 10)
       Deliver the first customer-facing extraction through read-heavy capabilities with clear fallback paths. This validates the platform, routing, replication, and operational model without changing transaction ownership.

- Build a catalogue read service fed from monolith-owned catalogue data through outbox or controlled replication.
- Replace nightly-only Lucene rebuilding with an independently operated search service that supports incremental index updates, aliases, blue/green indexes, and rapid rollback to the existing index.
- Run catalogue and search in shadow mode. Compare product availability, locale content, ranking, facets, response time, and zero-result rates against current behaviour.
- Shift traffic gradually by country and cohort. Keep the monolith catalogue/search route live until parity and peak tests pass.
- Introduce cache policies, invalidation events, stale-data limits, and cache-bypass operational controls.
- Do not make the new search service authoritative for price or stock. It displays explicitly versioned read models from their owning domains.
    12. Modernise inventory integration and availability reads (depends on: 7, 8, 9, 10)
       Separate warehouse file exchange from customer-facing inventory reads while preserving warehouse and order-system correctness. Inventory changes are operationally sensitive and require explicit freshness semantics.

- Build a warehouse integration adapter that validates, records, deduplicates, and acknowledges inbound and outbound files without changing warehouse contracts initially.
- Publish inventory-change events and create an availability read model for storefront and search use.
- Define country and fulfilment-node stock semantics, safety-stock rules, oversell tolerance, freshness targets, and customer messaging for stale or unavailable stock.
- Shadow-compare the new availability result with the monolith for all products and warehouses. Reconcile every discrepancy before traffic expansion.
- Preserve current monolith stock reservation and allocation authority until order and inventory ownership boundaries are fully designed.
- Provide an immediate fallback to monolith availability reads and a replayable file-processing recovery process.
    13. Discover and contain pricing and promotions (depends on: 2, 6, 7, 8, 9, 10)
       Treat pricing and promotions as the highest-risk business capability. First make its behaviour observable and testable; do not attempt a big-bang rewrite based on incomplete knowledge.

- Form a dedicated cross-functional squad with senior engineers, merchandising, finance, country representatives, customer support, and QA.
- Inventory all rules, stored procedures, configuration tables, overrides, scheduled jobs, manual back-office actions, and country-specific exceptions.
- Capture real production decision inputs and outputs into a privacy-safe decision log. Build a golden-master corpus covering products, customer segments, countries, currencies, dates, carts, vouchers, stacking, tax, and edge cases.
- Put the existing engine behind a versioned pricing façade. New callers use the façade even while it delegates to monolith logic.
- Build a new rules-evaluation candidate service only for well-understood rule slices. Shadow-evaluate and compare exact price, discount, explanation, and latency before any customer exposure.
- Require business sign-off, discrepancy classification, and financial-impact analysis before moving each rule slice. Keep a per-slice route-back switch to the legacy engine.
    14. Extract customer and loyalty capabilities safely (depends on: 7, 8, 9, 10)
       Move customer-facing identity-adjacent data only after privacy, consent, and data ownership are clear. Avoid introducing inconsistent account state across countries and channels.

- Define the canonical customer identifier, consent model, data-retention rules, subject-access and deletion workflows, and access-control model.
- Start with a replicated customer profile read service, then migrate bounded profile writes through a façade with idempotency and audit trails.
- Move loyalty functions in small slices, such as balance inquiry before accrual or redemption, using a ledger model and reconciliation against legacy balances.
- Support account-session compatibility across web, mobile, monolith, and new services throughout the transition.
- Reconcile customer records, consent states, and loyalty balances daily during migration. Route exceptions to trained operations staff.
- Retain a compatibility adapter for legacy back-office functions until those workflows are migrated or retired.
    15. Extract order views and bounded post-order workflows (depends on: 7, 8, 9, 10, 14)
       Create independently deployable order-related value without prematurely splitting the transactional checkout path. Start with event-driven reads and post-order processes that can tolerate asynchronous integration.

- Publish reliable order lifecycle events from the monolith using the outbox pattern.
- Build an order query service for customer-service, customer self-service, notifications, and selected back-office views. Validate it against monolith order history and live state.
- Extract bounded workflows such as notifications, selected return initiation, return-status tracking, and non-financial order enrichment where ownership is explicit.
- Preserve monolith order creation, payment capture coordination, cancellation authority, refund authority, and warehouse order export until their transition design is approved.
- Implement reconciliation for order counts, states, refunds, returns, notification delivery, and event lag.
- Ensure every new order-facing view identifies source freshness and has a monolith fallback for support staff.
    16. Create cart, checkout, and payment transition architecture (depends on: 7, 8, 9, 10, 11, 12, 13, 15)
       Prepare the revenue-critical transactional path through façade-first migration, exhaustive provider testing, and progressive traffic control. This stage must not force immediate service ownership transfer.

- Define cart identity, guest-to-account merge rules, session persistence, currency and country transitions, promotion snapshots, inventory checks, and checkout idempotency keys.
- Introduce a checkout façade that initially delegates to the monolith. Route storefront and mobile gradually while maintaining response and error compatibility.
- Isolate each payment provider behind versioned adapters with token handling, webhook verification, idempotent authorisation and capture, retry policy, reconciliation, and provider-specific fallback behaviour.
- Build a payment ledger and daily reconciliation process covering authorisations, captures, refunds, chargebacks, provider settlements, and orders.
- Shadow-run checkout orchestration and payment-adapter decisions where possible. Use provider test environments and controlled internal cohorts before customer traffic.
- Do not split the final order-creation transaction until failure-mode analysis, compensating actions, support procedures, and sale-peak load tests demonstrate acceptable risk.
    17. Transfer ownership through controlled data cutovers (depends on: 7, 8, 11, 12, 13, 14, 15, 16)
       Move write ownership one entity group at a time after services have proven read parity and operational maturity. Each cutover is a reversible state transition, not a one-time database migration.

- For each entity, document source of truth, writer cutover sequence, replication direction, API consumers, data-retention obligations, reconciliation rules, and rollback point.
- Use expand-contract schemas, backfills with checksums, change capture or outbox replication, dual-read validation, and carefully bounded write cutovers.
- Avoid unrestricted dual writes. During transition, route writes through one command owner that publishes changes reliably to dependent systems.
- Reconcile continuously by identifiers, row counts, hashes, financial totals, stock totals, and business state transitions. Define thresholds that automatically halt traffic expansion.
- Retain legacy data read access and compatibility APIs until all consumers are migrated and the defined observation period has passed.
- Schedule any high-risk ownership cutover outside sales protection windows, with an approved rollback rehearsal and staffed hypercare period.
    18. Execute progressive traffic migration and rollback drills (depends on: 4, 8, 10, 11, 12, 13, 14, 15, 16, 17)
       Move production traffic only through measured, reversible increments. Every migration uses the same operational playbook regardless of domain.

- Progress through dark launch, shadow comparison, employee cohort, low-risk country or cohort, 1%, 5%, 25%, 50%, and full traffic stages where appropriate.
- Define quantitative promotion criteria for each stage: error rate, latency, conversion, search quality, price parity, payment approval rate, order completion, inventory discrepancy, support contacts, and reconciliation lag.
- Automate route rollback and validate it with game days. Rollback must restore a known compatible route without data loss or customer-visible duplicate operations.
- Run failure injection for dependency loss, event delay, duplicate messages, provider timeout, cache failure, database failover, and warehouse-file replay.
- Maintain staffed hypercare after each material expansion, with business, support, and engineering representatives able to pause or reverse rollout.
- Freeze traffic increases before sales protection windows. Use those windows only for monitoring, capacity verification, defect fixes with approved exceptions, and rehearsed rollback readiness.
    19. Prepare peak-season resilience and capacity certification (depends on: 4, 5, 8, 11, 12, 13, 16, 18)
       Certify both the hybrid estate and fallback paths for January and July sales. A service is not production-ready if its rollback target cannot sustain the traffic it might receive.

- Forecast peak demand by country, channel, page type, checkout step, payment provider, product launch, and warehouse activity.
- Load-test the full hybrid path at at least 12x observed baseline plus agreed headroom, including gateway, caches, monolith, services, event platform, databases, search, payment adapters, and warehouse integration.
- Test traffic reversion from each service to monolith and confirm that the monolith, database, and legacy search can absorb the reverted load.
- Establish capacity reservations, autoscaling limits, database connection limits, cache warm-up procedures, provider rate-limit agreements, and operational staffing plans.
- Conduct sale-day simulations, incident command exercises, communications rehearsals, and business-continuity tests with payment providers and warehouse stakeholders.
- Obtain formal peak-readiness sign-off from engineering, operations, commerce, finance, warehouse, and customer support.
    20. Retire legacy paths and establish steady-state service governance (depends on: 17, 18, 19)
       Conclude the 12-month programme by removing only proven-obsolete paths and making service ownership sustainable. Retain legacy components where removal would create unjustified business risk.

- Decommission monolith endpoints, batch jobs, Lucene components, table access paths, and stored procedures only after consumer inventory, data archival, reconciliation, and rollback-retention periods are complete.
- Remove temporary replication and compatibility adapters in controlled releases. Update runbooks, diagrams, recovery procedures, and ownership records at the same time.
- Measure and reduce residual monolith coupling, direct database access, synchronous dependency chains, event lag, and operational toil.
- Establish quarterly architecture reviews, API and event lifecycle governance, service scorecards, dependency upgrade policies, resilience testing, and disaster-recovery exercises.
- Conduct a post-migration review against business outcomes, incident history, delivery lead time, cost, and peak performance. Prioritise any remaining pricing, checkout, order, or database decomposition work as a funded follow-on roadmap.
- Confirm that independently deployable services have named owners, on-call coverage, SLOs, operational documentation, and tested rollback or recovery procedures.

  Previous Proposal 3 (ID: b39f2a46-559f-4920-9664-540fcdb612f4, Agent: grok-4.6_initial_3, LLM: xai/grok-4.6):
    Estimated Complexity: high
    Success Metrics: - Zero unplanned downtime attributed to migration work across the 12 months.
- Every production cutover has a practised rollback that restores the previous path in minutes.
- January and July peak capacity at or above today's 12x headroom, with no extra error-budget burn on search, cart, checkout or payments.
- Feature throughput stays at or above the current two-week train; no programme-wide feature freeze.
- At least search, catalogue, identity, inventory, pricing, cart, checkout and OMS deploy independently of the monolith artefact.
- Dual-run mismatch rate for prices and stock below an agreed threshold before each traffic shift (target: 0 on money paths).
- Golden-journey pass rate 100% on critical paths before and after each cutover.
- Monolith database coupling reduced: no new cross-context joins; stored-procedure call volume on extracted domains at zero after ownership transfer.
- Mean time to revert a bad service release under 10 minutes via flags or routing.
    Steps (25):
    1. Charter, governance and non-negotiables
       Write a short **migration charter** that product, ops, finance and all five teams sign.

Feature work never stops. Only production risk is constrained.

- Name one accountable migration lead and a weekly steering forum.
- Ban big-bang rewrites, shared-database-first splits and un-reversible cutovers.
- Require a tested rollback for every production step.
- Keep the two-week monolith release train for features until a domain is fully extracted.
    2. Peak calendar and freeze protocol (depends on: 1)
       Protect **January and July** sales with hard engineering blackouts.

No extractions, schema splits or traffic switches in the six weeks before a sale or the two weeks after, unless they are already proven and idle.

- Publish the 12-month calendar in week one.
- Freeze means no new migration risk, not a feature freeze.
- Require a peak capacity rehearsal before each blackout.
- Give ops a veto on any change that could affect checkout, payments, stock or search.
    3. Baseline architecture, data and SLOs (depends on: 1)
       Measure the live system before changing it.

Build a factual map of the 2M-line monolith, the 1.2 TB database and the real traffic shape.

- Trace the top 30 user journeys and the 350 tables they touch.
- Record p50/p95/p99, error rates and 12x peak headroom per journey.
- Inventory stored procedures, cross-module joins and file exchanges.
- Tag every endpoint used by the storefront, mobile app and back-office.
    4. Delivery platform, flags and progressive delivery (depends on: 1)
       Give every team a **safe way to ship** without the 30-minute maintenance window.

New work deploys behind flags. Old work stays on the existing train until it is ready.

- Add feature flags, weighted routing and instant revert at the edge.
- Build CI that can later publish one artefact per service.
- Keep Java 8 on the monolith. Start new services on a current LTS.
- Provide preview environments that replay production-like traffic.
    5. Observability and error budgets (depends on: 3, 4)
       Instrument the monolith as if it were already many services.

You cannot extract what you cannot see.

- Add distributed tracing, RED metrics and structured logs with correlation IDs.
- Define SLOs for search, PDP, cart, checkout, payments and back-office.
- Page on error-budget burn, not on CPU.
- Dashboards must show monolith vs new service side by side for every cutover.
    6. Safety net: journeys, contracts and load (depends on: 3)
       Raise the net where extraction will cut.

Unit coverage at 25% is not enough. Protect behaviour, not lines.

- Record golden journeys for browse, price, cart, checkout, order, return and loyalty.
- Add contract tests on every mobile and storefront endpoint.
- Capture characterization tests around stored procedures before moving them.
- Automate a 12x peak load test and run it before each sale and each major cutover.
    7. Bounded contexts and extraction backlog (depends on: 3)
       Draw domain boundaries from the business, not from the package tree.

Sequence work by **risk and coupling**, not by fashion.

- Contexts: identity, catalogue, search, pricing, inventory, cart, checkout, orders, returns, loyalty, back-office.
- Extract read-mostly and already-async seams first (search, inventory files).
- Leave pricing and checkout until dual-run and reconciliation exist.
- Rank a 12-month backlog with a rollback story on every item.
    8. Team operating model without a freeze (depends on: 1, 7)
       Keep five domain teams. Stop treating the repo as a single ownership blob.

Each team ships features in the monolith **and** prepares its future service.

- Assign a service to own per team, plus a shared platform pair.
- Code owners and module walls inside the current repository first.
- A small platform group owns gateway, flags, events, CI and data tooling.
- Product still plans features; migration work is a percentage of each sprint, not a separate freeze.
    9. Modularise the monolith in place (depends on: 6, 7)
       Create seams before you create processes.

New code may not add cross-module joins or new stored-procedure coupling.

- Split packages by bounded context with compile-time walls.
- Replace in-process calls at boundaries with interfaces (branch by abstraction).
- Document and freeze the worst pricing and checkout internals; wrap them.
- Ban new features from reaching into another team's tables.
    10. Strangler facade and instant traffic rollback (depends on: 4, 5)
       Put a reverse proxy in front of every public and mobile endpoint.

Clients keep the same URLs. You choose monolith or service per route and per percentage.

- Preserve headers, sessions, cookies and the four languages.
- Shadow traffic before any live percentage.
- Rollback is a route change, not a redeploy, and must complete in minutes.
- Storefront SSR and the mobile app stay compatible until a later BFF if needed.
    11. Events, outbox and CDC backbone (depends on: 5, 9)
       Give the monolith a **reversible integration spine**.

Services must not call each other's databases. They subscribe to facts.

- Add an outbox in the same Postgres transaction as business writes.
- CDC from the monolith for tables you do not yet own.
- Standard event names for product, price, stock, customer, order and return.
- Idempotent consumers and a dead-letter process before the first extraction.
    12. Data-change playbook: dual-write, reconcile, roll back (depends on: 11)
       Treat every data move as a campaign with an abort switch.

The 1.2 TB database stays the system of record until a service proves otherwise.

- Dual-write with the monolith write winning on conflict during trial.
- Nightly and continuous reconciliation with row-level diffs.
- Never cut stored procedures until logic has an equivalent test harness.
- Rollback means stop writes to the new store and keep serving from Postgres.
    13. Extract search as the first service (depends on: 2, 8, 10, 11, 12)
       Replace the nightly Lucene rebuild with an independently deployed **search service**.

This is read-heavy, already eventually consistent, and off the payment path.

- Index from catalogue and price events, not from a nightly dump.
- Shadow queries against current Lucene until precision/recall match.
- Shift traffic 1% → 10% → 50% → 100% with instant route rollback.
- Keep the old index warm through the next sale as a cold standby.
    14. Extract catalogue read models (depends on: 13)
       Serve product, media and localisation from a catalogue service.

Writes can stay in the monolith until editors have a new path.

- Build country and language-specific read models for eight markets.
- Keep one product identity so pricing, stock and search stay aligned.
- Cut storefront and mobile read traffic via the strangler.
- Do not move merchandising tools until reads are stable.
    15. Extract identity, accounts and session (depends on: 8, 10, 12)
       Pull login, profile, addresses and session behind a dedicated service.

Mobile and web keep the same auth cookies or tokens during the switch.

- Migrate sessions without forced logouts.
- Dual-read loyalty points until that domain is extracted.
- GDPR/export and deletion flows must work in both systems.
- Rollback restores monolith auth with no password resets.
    16. Extract inventory and warehouse sync (depends on: 8, 11, 12)
       Replace the 15-minute file exchange with an inventory service that still talks to the warehouse.

The warehouse interface stays file-based until they can change. Your side becomes events.

- Service owns ATP, reservations and oversell rules.
- Adapter keeps the existing file contract so warehouse risk is zero.
- Cart and checkout read stock from the service via API or replica.
- Prove no extra oversell versus today's 15-minute lag before a sale.
    17. Pricing archaeology and dual-run harness (depends on: 6, 9)
       Do not extract the 200k-line pricing module until you can prove equivalence.

Nobody fully understands country rules. Tests must become the spec.

- Capture production price traces for all eight countries and three currencies.
- Build a harness that replays promotions, baskets and edge SKUs.
- Freeze behavioural snapshots; new promo features implement twice until cutover.
- Only then wrap pricing behind an interface inside the monolith.
    18. Extract pricing and promotions behind dual-run (depends on: 14, 17, 12)
       Run the new pricing service in **shadow** until it matches the monolith on live baskets.

Checkout keeps using monolith prices until the error budget is clean.

- Compare every quote; alert on any currency, tax or promo mismatch.
- Shift read traffic first, then write of promo usage.
- Keep the monolith engine deployable as rollback through the next two sales.
- Country-specific rules move last, one market at a time if needed.
    19. Extract cart (depends on: 15, 16, 18)
       Move the cart after identity, catalogue, stock and price reads are stable.

Cart is stateful. Lose no baskets during cutover.

- Dual-write carts; reconcile abandoned and active baskets.
- Preserve promo application using the dual-run price API.
- Session migration must survive app versions in the wild.
- Rollback reattaches baskets to the monolith cart tables.
    20. Extract checkout and payment orchestration (depends on: 19)
       Strangle checkout without touching the three payment providers in one step.

A thin orchestration service talks to existing provider integrations first.

- Keep PCI and provider contracts stable; wrap, do not rewrite.
- Idempotent order placement with an outbox to OMS.
- Canary by country and by payment method.
- Rollback is route-plus-flag; in-flight payments complete on the old path.
    21. Extract order management (depends on: 20)
       Move post-purchase order state once checkout emits reliable events.

OMS must survive 12x peaks and warehouse files.

- Order of record shifts only after reconciliation is clean for a full weekly cycle.
- Back-office screens can still read a projection while writes move.
- Returns and finance reports stay correct during dual-run.
- Keep monolith OMS as standby through one sale after cutover.
    22. Extract returns, loyalty and remaining back-office (depends on: 15, 21)
       Peel remaining domains once orders and identity are independent.

Staff of 300 must not get a big-bang UI change.

- Returns service consumes order events and drives refunds via payment facade.
- Loyalty becomes the owner of points with dual-write from checkout.
- Back-office gets BFFs or modular UIs per domain, not a new monolith.
- Train staff per screen group; keep old screens until the new ones match.
    23. Split data ownership and retire stored procedures (depends on: 16, 18, 21)
       Give each stable service its **own schema or database** only after traffic and reconciliation are boring.

Shared Postgres is allowed during transition. It is not the end state.

- Move table ownership context by context; no cross-service joins.
- Rewrite stored procedures into service code with the characterization harness.
- Shrink the 1.2 TB monolith database as tables go dark.
- Rollback is restoring replication to the monolith copy, practised in staging.
    24. Independent deploy pipelines and repository split (depends on: 8, 23)
       When a service is independently releasable, stop bundling it into the fortnightly artefact.

Teams ship on demand with automated checks and progressive delivery.

- One pipeline per service: test, canary, promote, revert.
- Split repos only after module walls and CI already work in the monorepo.
- Contract tests gate consumer and provider deploys.
- The remaining monolith keeps the old two-week train until it is small.
    25. Peak rehearsals, chaos and residual shrink (depends on: 2, 22, 24)
       Prove **12x capacity** on the mixed architecture before each sale.

Then keep shrinking the monolith so it is a shell, not a risk.

- Game-days: provider failure, CDC lag, flag revert, search fallback, stock file delay.
- Scale tests on checkout, search and inventory with production-sized data.
- Delete dead modules, jobs and tables only after two quiet weeks.
- End state: independently deployable services, instant rollback still in place, no unplanned downtime attributed to the programme.

  Previous Proposal 4 (ID: 9219a465-1887-4823-81a8-3cbfc3bb545b, Agent: deepseek-v4-pro_initial_4, LLM: deepseek/deepseek-v4-pro):
    Estimated Complexity: high
    Success Metrics: - 100% of critical customer journeys remain within SLOs during migration; no unplanned downtime outside planned windows.
- Every extraction step has a rehearsed rollback path that restores monolith behaviour in under 5 minutes.
- Peak-season capacity maintained: January and July sales complete without capacity-related errors, with peak traffic at least 12x baseline and error rate <= 0.1%.
- By month 12, at least 8 core business capabilities are deployed as independently deployable services from separate repositories with separate data ownership.
- Monolith code is reduced by at least 60%, and the remaining monolith no longer owns migrated data or executes migrated stored procedures.
- Deployment frequency increases from one release every two weeks to daily per service; lead time for changes decreases from weeks to hours.
- Test coverage on changed code reaches at least 80%; critical pricing and checkout paths have contract and parity tests.
- Zero data loss or irreversible data corruption during migration; reconciliation discrepancies are below 0.01% of records.
- Feature delivery velocity remains at least equal to pre-migration levels; no feature freeze is imposed.
- No cross-service direct database joins remain for migrated capabilities; all service data access happens through APIs or events.
    Steps (19):
    1. Baseline and decompose the monolith into bounded contexts
       Capture the current behaviour, data model, and operational risks before changing anything. The output is a shared map that justifies every later cutover.
- Inventory all modules, endpoints, database tables, stored procedures, cross-module joins, external integrations, and batch jobs.
- Map business capabilities to bounded contexts and identify candidate service seams and data owners.
- Record every country/currency/language variation, especially the 200k-line pricing and promotions module.
- Capture the peak-season calendar, current deployment windows, known failure modes, and rollback mechanisms.
- Create a risk register with blast radius and rollback criteria for each candidate extraction.
    2. Define target service architecture and migration sequence (depends on: 1)
       Agree the target state and the guardrails before building any new service.
- Publish target decomposition: storefront, catalogue/search, pricing/promotions, cart/checkout, orders, inventory, customers/loyalty, returns, back-office.
- Define synchronous APIs, asynchronous events, idempotency, retries, sagas, and eventual consistency where required.
- Define data ownership and database-per-service strategy; prohibit cross-service joins and direct access to another service's tables.
- Define API versioning, security, tenancy, and country-specific routing.
- Choose migration sequence: start with low-risk read-heavy capabilities and delay peak-sensitive cutovers until outside sales windows.
- Set the rollback requirement: every change must be behind a flag or reversible migration with rehearsed rollback.
    3. Establish observability, SLOs and production load testing (depends on: 1)
       Make the current system measurable so cutovers are based on data, not hope.
- Add structured logs, metrics, and distributed tracing to the monolith and future services.
- Define SLOs and error budgets for storefront, catalogue, cart, checkout, payments, and order management.
- Add synthetic transactions and real-user monitoring for 8 countries, 3 currencies, and 4 languages.
- Build a performance test environment that replays production-like traffic at peak 12x volume.
- Create dashboards for golden signals, slow queries, stored procedure hotspots, and cache/index health.
    4. Build zero-downtime CI/CD and database migration automation (depends on: 2)
       This is the safety rail for every later step: frequent, reversible, low-risk deployments.
- Replace the biweekly single-artifact release with a pipeline supporting per-service builds, automated tests, security scans, and deployment.
- Introduce canary and blue-green deployment with automated rollback based on SLOs and error budgets.
- Add expand/contract database migration patterns: first add new schema, dual-write or synchronise, switch reads, then remove old schema in a later release.
- Ensure every service change is independently deployable in minutes, with no planned maintenance window.
- Use infrastructure-as-code and immutable artifacts for all environments.
    5. Strengthen tests and add contract testing before cutting seams (depends on: 3, 4)
       Raise confidence in behaviour without freezing features, focusing on seams to be extracted.
- Build characterization tests around current API endpoints, stored procedures, pricing rules, and checkout flows.
- Add consumer-driven contract tests between the monolith and new services.
- Introduce mutation testing and enforce at least 80% coverage on changed code.
- Add data-migration tests, reconciliation tests, and performance regression gates to CI/CD.
- Keep a long-running dual-read and diff harness for later services.
    6. Introduce traffic routing and feature flag platform (depends on: 4, 5)
       Enable gradual migration and instant rollback without redeploying the entire monolith.
- Deploy a feature flag system and edge/API gateway that can route traffic by customer, country, currency, language, percentage, and header.
- Add dark-launch capability to send shadow traffic to new services while the monolith remains source of truth.
- Implement kill switches that revert to monolith paths in one action.
- Integrate flags with SLO dashboards and deployment rollback.
    7. Extract customer accounts and loyalty as pilot service (depends on: 2, 3, 4, 5, 6)
       Prove the extraction playbook on a well-bounded, lower-risk capability before touching the most complex modules.
- Create a customer service owning customer, address, and loyalty data; expose a REST API with the same contracts.
- Move related monolith code behind an anti-corruption layer; run dual-writes or CDC to keep data in sync.
- Use expand/contract database migration: retain monolith tables temporarily, synchronise with the service, then switch reads/writes by flag.
- Launch to a small country and a small traffic percentage; monitor SLOs and rollback if errors exceed the error budget.
- Use the pilot to refine templates, runbooks, and training for other teams.
    8. Extract catalogue and search into a dedicated service (depends on: 3, 4, 5, 6, 7)
       Move the read-heavy catalogue and search path first, as it is valuable and relatively safe if done in shadow mode.
- Build a catalogue/search service that owns product, category, and search data; maintain the Lucene index within the service or via a dedicated index.
- Synchronise catalogue data from the monolith through CDC or events; stop cross-module joins.
- Serve storefront and mobile via the new catalogue/search API; run shadow reads against the monolith and compare.
- Route reads progressively by country and language and validate search quality, latency, and conversion.
- Keep the monolith fallback and flag-based rollback until after the peak readiness gate.
    9. Extract pricing and promotions with dual-run comparison (depends on: 7, 8)
       The most complex module; migration must be based on observed behavioural equivalence.
- Build a pricing/promotions service with country-specific rules as versioned configuration or domain rules.
- Run the new service in shadow mode on all checkout/cart/catalogue calls and compare every calculation with the monolith for months before cutover.
- Treat any divergence as a defect; require 100% parity on sampled and historical promotion scenarios before routing live traffic.
- Expose a pricing API and route live reads/writes only by country and promotion type, with immediate rollback.
- Keep the monolith promotion engine available until after all peak seasons.
    10. Extract inventory service and modernise warehouse integration (depends on: 7)
       Replace the 15-minute file exchange with safer, event-driven inventory updates while keeping the old path as fallback.
- Build an inventory service owning stock levels, reservations, and warehouse sync logic.
- Integrate with the warehouse system via API or events and keep the file exchange running in parallel for dual sync.
- Expose inventory availability and reservation APIs for cart, checkout, and back-office.
- Run reconciliation between the old file batch and the new event flow for all SKUs; eliminate divergence before cutover.
- Route inventory consumers to the service progressively, maintaining the monolith fallback.
    11. Extract cart and checkout service (depends on: 7, 8, 9, 10)
       Move the highest-value transaction path only after its dependencies are available and proven.
- Build a cart/checkout service that owns cart state and checkout workflow; it calls customer, catalogue, pricing, and inventory APIs with fallbacks.
- Integrate the three payment providers through adapters; implement idempotency, retries, and reconciliation.
- Use saga or orchestration for payment, inventory reservation, and order creation.
- Route by country, currency, and traffic percentage; start with one payment provider and one country.
- Rehearse rollback to monolith checkout and validate that no cart or payment is lost.
    12. Peak readiness gate before first sales peak (depends on: 7, 8, 9, 10, 11)
       Protect the first peak by freezing risky cutovers while allowing normal feature work through flags.
- Freeze new service cutovers and irreversible data migrations for four weeks before and during the peak.
- Run production-like load tests at 12x baseline with monolith and new services in their current routing ratios.
- Rehearse rollback for every extracted service and confirm the monolith fallback handles full load.
- Pre-scale infrastructure to at least 30% above expected peak.
- Keep on-call and war-room runbooks ready; certify only if all SLOs pass in load tests.
    13. Extract order management service after first peak (depends on: 11, 12)
       Move order persistence and lifecycle after the first peak, using events from checkout and inventory.
- Build an order service owning orders and order lines; consume order-placed events from checkout and payment.
- Replace monolith order creation and status update code behind flags.
- Backfill historical orders into the service and run reconciliation.
- Route order read/write traffic progressively; maintain the monolith fallback.
- Ensure returns and customer service integration remains consistent.
    14. Extract returns service (depends on: 13)
       Move returns and refunds out of the monolith once order and inventory services are stable.
- Build a returns service owning return requests, labels, refund settlements, and status.
- Integrate with order, inventory, and payment services via APIs and events.
- Migrate business rules country-by-country with dual-run comparison.
- Keep the monolith fallback and rollback for all return journeys.
    15. Extract back-office capabilities (depends on: 13, 14)
       Decouple internal staff tools from the monolith so operations run on the new services.
- Build a new back-office UI or modular back-office services that use the same APIs as storefront services.
- Migrate catalogue, pricing, inventory, order, customer, and returns management screens incrementally.
- Route the 300 staff users per function and country; keep the old monolith back-office available during transition.
- Automate manual processes and remove direct SQL access to migrated data.
    16. Peak readiness gate before second sales peak (depends on: 13, 14, 15)
       Repeat the readiness gate for the second peak with more services in production.
- Freeze cutovers and irreversible migrations four weeks before and during the peak.
- Run full production-like load test at 12x peak with current routing ratios.
- Rehearse rollback for order management, returns, and back-office services.
- Verify capacity headroom and failover to monolith if required.
- Certify only if all critical SLOs are met under peak stress.
    17. Decommission monolith code and data ownership for migrated modules (depends on: 16)
       Remove the old implementation only after both peaks and after all services have proven parity.
- Remove feature flags and dark-launch paths for customer, catalogue, pricing, inventory, cart/checkout, orders, and returns.
- Drop or archive monolith tables and stored procedures for migrated modules after data reconciliation.
- Delete monolith code paths and reduce monolith size; verify no cross-module joins remain.
- Run final reconciliation and zero-downtime data archival.
    18. Final cutover, contract freeze and monolith retirement (depends on: 17)
       Complete the strangler migration so the monolith stops serving business traffic.
- Switch any remaining traffic to services and remove edge routing to the monolith.
- Decommission monolith deployments; maintain a read-only archive for audit.
- Finalise API contracts, versioning, and service ownership.
- Complete security reviews and production runbooks for all services.
    19. Continuous improvement and governance handover (depends on: 18)
       Make the new architecture the default and keep it safe after migration.
- Embed service ownership: each team owns build, deploy, and operate with SLOs.
- Review cost, performance, and reliability; tune autoscaling and capacity for the next peak.
- Update architecture decision records, runbooks, and onboarding material.
- Establish a light-touch governance board for API and data contracts.

  Previous Proposal 5 (ID: 733858cb-4dad-4bb6-85c9-79a3e6d968d9, Agent: qwen3.8-max_initial_5, LLM: alibaba/qwen3.8-max):
    Estimated Complexity: high
    Success Metrics: - Zero unplanned downtime throughout the 12-month migration; all maintenance performed via blue-green or canary cutover.
- Every extraction step is reversible within 5 minutes via feature-flag rollback, validated at least once in production before full cutover.
- Deployment frequency increases from bi-weekly to at least daily per service by month 4.
- Peak-load capacity sustained at 12× normal traffic (≈ 480 000 orders/day) with p99 latency ≤ 800 ms for checkout and ≤ 400 ms for storefront during January and July sales.
- Test coverage on all migrated code paths reaches ≥ 80 %; contract tests exist for every inter-service boundary.
- Monolith codebase reduced from 2 M lines to 0 lines in production by end of month 12.
- All 350 tables are owned by exactly one service; zero cross-service direct database joins remain.
- The three payment providers maintain ≥ 99.95 % successful transaction rate throughout the migration.
- Back-office availability for 300 staff ≥ 99.9 % during business hours across all 8 countries.
- Mean time to recovery (MTTR) for any single-service incident ≤ 10 minutes.
- No degradation in order-accuracy rate (≥ 99.99 %) or inventory reconciliation accuracy (≥ 99.9 %) at any point during the migration.
- Customer-facing error rate (5xx) stays below 0.1 % across all 8 countries, 3 currencies, and 4 languages throughout the programme.
    Steps (20):
    1. Full-Scope Discovery and Dependency Mapping
       Perform a **complete technical and organisational audit** of the monolith before any code changes.

- Run static-analysis tools (jQAssistant, ArchUnit, custom SQL scripts) to produce a module-level dependency graph across all 2 M lines of Java and all 350 PostgreSQL tables.
- Catalogue every stored procedure, trigger, and cross-module join; classify each as *local*, *cross-module read*, or *cross-module write*.
- Interview each of the five teams to document tribal knowledge, especially the pricing & promotions rules (200 K lines, country-specific logic).
- Map all external integrations: three payment providers, warehouse file exchange, mobile-app endpoints, back-office UI routes.
- Record current performance baselines: p50 / p95 / p99 latency per endpoint, throughput, DB query plans for the top-100 queries.
- Deliverable: a living architecture dossier stored in a shared wiki, updated throughout the migration.
    2. Build CI/CD Pipelines and Feature-Flag Platform (depends on: 1)
       Create the **deployment and release-safety infrastructure** that every later step depends on.

- Stand up a CI/CD stack (e.g. GitLab CI or GitHub Actions → ArgoCD) capable of building, testing, and deploying individual modules independently.
- Introduce a feature-flag platform (LaunchDarkly, Flagsmith, or Unleash) wired into the monolith via a thin SDK; every new or changed code path ships behind a flag.
- Define branching strategy: one repo per future service, plus the existing monorepo during the transition period.
- Automate canary and blue-green deployment patterns so every release can be rolled back in under five minutes.
- Target: reduce the two-week release cycle to **daily deployable** by end of this step.
    3. Establish Observability, Tracing, and SLO Baseline (depends on: 1)
       Instrument the monolith so that **every subsequent extraction is measurable** and regressions are caught within minutes.

- Deploy OpenTelemetry agents across all application nodes; export traces, metrics, and structured logs to a central stack (Grafana Tempo + Prometheus + Loki, or Datadog).
- Define SLOs per domain: storefront p99 < 400 ms, checkout p99 < 1.2 s, search p95 < 300 ms, back-office p95 < 2 s.
- Build real-time dashboards per SLO with alerting thresholds; wire alerts to on-call rotation.
- Implement synthetic transaction monitoring covering the critical user journeys (browse → cart → checkout → payment → confirmation) across all 8 countries, 3 currencies, and 4 languages.
- Establish an error-budget policy: any extraction step that breaches its SLO budget is automatically rolled back.
    4. Automated Testing Uplift and Contract-Test Foundation (depends on: 2)
       Raise test coverage from **25 % to at least 60 %** on the paths that will be touched first, and introduce contract testing.

- Use mutation testing (PIT) to identify the highest-risk untested paths; prioritise checkout, payment, and inventory flows.
- Add integration tests using Testcontainers with a seeded copy of the production schema.
- Introduce Pact (or Spring Cloud Contract) for consumer-driven contract tests between every pair of modules that will become separate services.
- Build a regression suite of end-to-end smoke tests runnable in < 15 minutes, executed on every deploy.
- Define a policy: no extraction proceeds unless the affected module reaches the agreed coverage threshold.
    5. Team Topology Realignment and Governance Model (depends on: 1)
       Reorganise the five teams into **stream-aligned, domain-owned squads** and agree on governance rules for the migration.

- Map each team to a bounded context: (1) Storefront & Search, (2) Pricing & Promotions, (3) Cart, Checkout & Payments, (4) Order Management, Inventory & Returns, (5) Customer, Loyalty & Back-Office.
- Assign a Platform/Enablement guild (2–3 senior engineers drawn across teams) responsible for shared infra, libraries, and cross-cutting concerns.
- Agree on API governance: versioning policy (URL-path major, header minor), deprecation window (minimum 90 days), and an internal API catalogue.
- Set up a weekly cross-team architecture sync and a migration-risk register reviewed every sprint.
- Define the rollback decision tree: who can trigger a rollback, under what SLO breach, and the communication protocol.
    6. Strangler-Fig Gateway and Anti-Corruption Layer (depends on: 2, 3)
       Deploy an **API gateway in front of the monolith** that will route traffic to either the legacy code or the new services, enabling incremental extraction.

- Place a reverse-proxy / service mesh layer (e.g. Kong, Envoy via Istio, or AWS ALB + App Mesh) in front of the existing load balancer.
- Implement an Anti-Corruption Layer (ACL) service that translates between the monolith's internal models and the new service APIs.
- Configure the gateway to route by URL pattern, header, or feature flag; default route goes to the monolith.
- Support traffic mirroring (shadow traffic) so new services can be validated against live production traffic before receiving real requests.
- All mobile-app and back-office traffic passes through the gateway from day one; server-rendered pages are proxied transparently.
    7. Database Decomposition Strategy and Shared-Data Refactor (depends on: 1, 4)
       Prepare the **1.2 TB PostgreSQL database** for eventual per-service ownership without a big-bang migration.

- Classify all 350 tables by bounded context using the dependency map from S1.
- Eliminate cross-module joins at the application layer first: replace them with service calls or denormalised read models.
- Convert stored procedures that span contexts into application-level logic behind the ACL; keep single-context procedures temporarily.
- Introduce an internal event log (outbox pattern) on the existing database: every state change publishes a row to an `outbox` table, later relayed to a message broker.
- Define the target data-ownership matrix: which service will own which tables, and which data will be replicated read-only.
- Plan a dual-write / change-data-capture (CDC) strategy using Debezium so that during transition both old and new stores stay consistent.
    8. Event-Driven Backbone and Async Messaging Layer (depends on: 6, 7)
       Stand up the **messaging infrastructure** that decouples services and replaces synchronous cross-module calls.

- Deploy Apache Kafka (or AWS MSK) with topics per bounded context: `catalogue-events`, `order-events`, `inventory-events`, `pricing-events`, `customer-events`.
- Implement the transactional outbox relay (Debezium → Kafka Connect) so the monolith can publish domain events without code changes to business logic.
- Define event schemas in a central Schema Registry (Avro / Protobuf) with backward-compatibility enforcement.
- Add idempotent consumer patterns and dead-letter queues from day one.
- Validate throughput: the backbone must sustain 12× peak (≈ 480 000 orders/day equivalent event volume) with headroom.
    9. Containerisation and Kubernetes Platform Readiness (depends on: 2, 3)
       Package the monolith and prepare a **Kubernetes-based runtime** for all future services.

- Dockerise the existing monolith (multi-stage build, slim JRE image) and deploy it to a Kubernetes cluster alongside the gateway.
- Provision namespaces per bounded context, with network policies enforcing that only the gateway and the ACL can reach the monolith.
- Configure horizontal pod autoscaling, pod disruption budgets, and resource quotas sized for 12× peak.
- Set up a service mesh (Istio or Linkerd) for mTLS, traffic splitting, circuit breaking, and retry policies.
- Run a load test replicating the January-sale profile (12× normal traffic) to validate the platform before any service extraction.
    10. Extract Customer Accounts and Loyalty Service (Wave 1) (depends on: 4, 6, 7, 8, 9)
       Carve out the **lowest-risk, well-bounded domain** first to validate the full extraction playbook.

- Build a new `customer-service` (Java 21 / Spring Boot 3 or Kotlin) exposing REST + gRPC APIs for registration, authentication, profile, and loyalty points.
- Migrate the relevant 15–20 tables to a dedicated PostgreSQL instance using the CDC dual-write pattern from S7.
- Place the service behind the ACL; route traffic via feature flags starting at 1 % → 10 % → 50 % → 100 % over two weeks.
- The monolith continues to serve as fallback; a single flag flip routes 100 % back.
- Validate contract tests, SLO dashboards, and rollback procedure end-to-end.
- This extraction serves as the **reference implementation** for all subsequent waves.
    11. Extract Catalogue and Search Service (Wave 2) (depends on: 10)
       Replace the nightly Lucene rebuild with a **real-time search and catalogue service**.

- Build a `catalogue-service` owning product data, categories, and media references; use CDC from the monolith DB during transition.
- Replace Lucene with Elasticsearch or OpenSearch; index updates driven by Kafka events instead of the nightly batch.
- Expose search and browse APIs through the gateway; server-rendered storefront pages call the new API via the ACL.
- Migrate in two sub-phases: (a) read-only catalogue and search behind flags, (b) write path (product updates from back-office) once reads are stable.
- Keep the legacy Lucene index warm for instant rollback for 60 days.
- Validate that search latency meets the p95 < 300 ms SLO across all 4 languages.
    12. Extract Inventory and Warehouse Sync Service (Wave 3) (depends on: 10)
       Isolate the **inventory domain and its 15-minute file-exchange** with the warehouse system.

- Build an `inventory-service` owning stock levels, reservations, and warehouse synchronisation.
- Replace the file-based exchange with an event-driven adapter: the service consumes warehouse updates via SFTP poll or API and publishes `inventory-updated` events to Kafka.
- During transition, run the adapter in parallel with the legacy file job; reconcile counts nightly.
- Checkout and order-management modules consume inventory availability via synchronous gRPC (with circuit breaker) and asynchronous events for reservation confirmations.
- Migrate stock tables using CDC; rollback path re-points reads to the monolith tables.
- Validate under 12× peak load: inventory checks must not become a bottleneck during flash sales.
    13. Deep Analysis and Rule Documentation for Pricing & Promotions (depends on: 1)
       Before touching the **most complex 200 K-line module**, invest in understanding and documenting its rules.

- Pair domain experts from each of the 8 country teams with developers to walk through every pricing rule, promotion type, and country-specific override.
- Produce a machine-readable rule catalogue (decision tables or a lightweight DSL) that captures all 200+ identified rules.
- Identify dead code, redundant branches, and rules that have not fired in the last 24 months (use production logging and feature-flag data).
- Classify rules into: (a) universal, (b) country-specific, (c) campaign/temporary.
- Define the target architecture: a `pricing-service` with a rules engine (Drools, Easy Rules, or a custom evaluation pipeline) externalised from application code.
- Deliverable: a signed-off rule specification document that all five teams agree represents current behaviour.
    14. Extract Pricing and Promotions Service (Wave 4) (depends on: 11, 12, 13)
       Rebuild the **highest-risk module** as an independent service using the documented rule set from S13.

- Build a `pricing-service` with a pluggable rules engine; encode the rule catalogue from S13 as configuration rather than hard-coded Java.
- Expose two API surfaces: synchronous price calculation (called by cart/checkout) and asynchronous promotion evaluation (event-driven for campaign changes).
- Run the new service in **shadow mode** for 4–6 weeks: every pricing request is sent to both the monolith and the new service; a comparator flags discrepancies.
- Only after the discrepancy rate drops below 0.01 % over two full weeks (including a weekend) begin traffic shifting via feature flags.
- Migrate pricing tables via CDC; keep monolith pricing logic compilable and deployable as rollback for 90 days.
- Assign dedicated on-call coverage for the first 30 days post-cutover.
    15. Extract Cart, Checkout, and Payment Service (Wave 5) (depends on: 14)
       Separate the **revenue-critical checkout flow** into its own service with hardened payment integration.

- Build a `checkout-service` owning cart state, checkout orchestration, and integration with the three payment providers.
- Cart state moves to a dedicated data store (Redis for transient cart, PostgreSQL for persisted orders) with CDC from the monolith during transition.
- Payment-provider integrations are wrapped in an adapter layer with circuit breakers and idempotency keys; failover order between providers is configurable per country.
- Migrate in sub-phases: (a) cart operations, (b) checkout orchestration, (c) payment capture and confirmation.
- Run chaos-engineering tests (payment-provider timeout, partial failure) before enabling real traffic.
- Rollback: feature flag routes checkout back to monolith; in-flight transactions are drained gracefully.
    16. Extract Order Management and Returns Service (Wave 6) (depends on: 15)
       Move **post-purchase order lifecycle and returns processing** into a dedicated service.

- Build an `order-service` consuming `order-placed` events from checkout; it owns order state machine, fulfilment tracking, and returns workflow.
- Integrate with the inventory service for reservation release and with the warehouse adapter for shipping updates.
- Back-office order views call the new service API through the gateway; legacy views remain as fallback.
- Migrate order and returns tables via CDC; reconcile daily during the 60-day dual-run window.
- Validate that the returns process (including cross-border returns across the 8 countries) works identically.
- Rollback re-routes order queries to the monolith; event replay ensures no order is lost.
    17. Extract Back-Office and Admin Portal (Wave 7) (depends on: 16)
       Deliver a **modern back-office** for the 300 staff users, consuming the new service APIs.

- Build a new back-office frontend (React or Vue SPA) backed by a thin BFF (Backend-for-Frontend) that aggregates calls to catalogue, pricing, order, inventory, and customer services.
- Migrate back-office routes incrementally via the gateway; legacy server-rendered admin pages remain accessible.
- Implement role-based access control (RBAC) and audit logging as cross-cutting concerns in the BFF.
- Run parallel operation for 4 weeks: staff use the new portal with a feedback channel; legacy portal stays one click away.
- Decommission legacy admin screens only after 30 days of zero critical issues.
- Provide training sessions and documentation for all 300 back-office users.
    18. Storefront Modernisation and Mobile-App API Alignment (depends on: 11, 14, 15)
       Update the **customer-facing storefront and mobile-app integration** to consume the new service layer.

- Refactor the server-rendered storefront to call service APIs via the gateway instead of hitting monolith endpoints directly.
- Introduce a Storefront BFF that aggregates catalogue, pricing, cart, and customer data for page rendering.
- Ensure the mobile app switches to the new API version behind the gateway; enforce backward compatibility for two app-release cycles.
- Implement edge caching (CDN + Varnish) for catalogue and search responses to protect services during 12× peaks.
- Validate all 4 language / 3 currency combinations through automated E2E tests.
- Rollback: gateway routes storefront traffic back to the monolith rendering path.
    19. Peak-Season Load Testing and Resilience Validation (depends on: 9, 15, 16)
       Prove the platform sustains **12× peak load** before the January and July sales windows.

- Build a load-test suite (Gatling or k6) replicating the full user journey across all 8 countries, including promo-code-heavy scenarios.
- Execute a full 12× load test in a staging environment that mirrors production topology, data volume (1.2 TB replica), and service versions.
- Run chaos-engineering experiments: kill random pods, introduce network latency, take a payment provider offline, simulate Kafka broker loss.
- Validate autoscaling: confirm that pod counts scale to handle peak within 90 seconds and scale down gracefully.
- Produce a signed-off capacity report; any component that fails the 12× test blocks go-live.
- Schedule this step at least 3 weeks before each peak season (early December for January sales, early June for July sales).
    20. Monolith Decommission and Final Data Migration (depends on: 16, 17, 18, 19)
       Retire the legacy monolith **only after all traffic is served by the new services**.

- Verify that zero production requests route to the monolith for 30 consecutive days.
- Perform a final data reconciliation: compare monolith DB checksums against service-owned databases.
- Archive the monolith codebase and database; retain read-only access for 12 months for audit and compliance.
- Decommission monolith infrastructure; reclaim compute and storage resources.
- Update all runbooks, on-call rotations, and disaster-recovery plans to reference the new service topology.
- Conduct a cross-team retrospective documenting lessons learned, technical debt incurred, and future improvement backlog.

Please, considering the previous proposals as ideas that could be considered, focus on the main objective and generate an IMPROVED proposal or a completely DIFFERENT perspective if you deem it appropriate. Only if you consider any of them is amazing and impossible to improve, answer with the same proposal.

The plan has these parts:
- "steps": the list of steps, each with "step_id" (a short label unique within this proposal, such as S1, S2, S3), "title" (a short, descriptive title), "description" (what the step does and how, in Markdown: a short opening paragraph and then, if there are several concrete things to say, a bullet list) and "dependencies" (the step_ids of the other steps of THIS proposal that must be completed first; empty if none).
- "estimated_complexity": "low", "medium" or "high".
- "success_metrics": clear and measurable success metrics, one per line as a bullet list.

Think deeply about the problem, considering all aspects and implications. Develop a comprehensive understanding of what needs to be done to achieve the objective, then produce the plan.

Answer with a single JSON object, and nothing else, that conforms to this JSON schema:
{"description": "The structure every proposing agent must answer with.", "properties": {"steps": {"items": {"description": "One step of a proposed plan. Dependencies are step_ids of the same proposal.", "properties": {"step_id": {"title": "Step Id", "type": "string"}, "title": {"title": "Title", "type": "string"}, "description": {"title": "Description", "type": "string"}, "dependencies": {"items": {"type": "string"}, "title": "Dependencies", "type": "array"}}, "required": ["step_id", "title", "description", "dependencies"], "title": "Step", "type": "object", "additionalProperties": false}, "title": "Steps", "type": "array"}, "estimated_complexity": {"title": "Estimated Complexity", "type": "string"}, "success_metrics": {"title": "Success Metrics", "type": "string"}}, "required": ["steps", "estimated_complexity", "success_metrics"], "title": "ProposalContent", "type": "object", "additionalProperties": false}

{"estimated_complexity": "high", "success_metrics": "- Zero unplanned customer-facing downtime attributable to migration work during the 12-month programme.\n- Every production cutover has a rehearsed rollback that can be initiated within 15 minutes and preserves financial, order, payment, and inventory integrity.\n- January and July sales complete with at least pre-migration peak availability, conversion rate, payment approval rate, and order throughput at 12x normal load.\n- The hybrid platform sustains 12x observed normal load plus agreed headroom in full-path load and failover tests before each sales period.\n- At least eight core capabilities are independently deployable by month 12: catalogue/search, inventory, customer/loyalty, pricing, cart/checkout, payments, orders, and returns.\n- Deployment frequency reaches at least daily per service, with no mandatory monolith maintenance window required for routine compatible releases.\n- Test coverage on changed code reaches at least 80%, and critical checkout, payment, pricing, stock, refund, and search scenarios have 100% contract and parity coverage.\n- Pricing and promotion parity for any migrated rule slice is at least 99.99% against the golden-master corpus, with all remaining differences explicitly approved by business owners.\n- Reconciliation identifies less than 0.01% unresolved record discrepancies and zero unresolved financial or stock discrepancies at each cutover.\n- Mean time to detect critical customer-journey failures is below 5 minutes, and mean time to restore or roll back migration-related severity-one incidents is below 30 minutes.\n- Feature delivery continues throughout the programme, with planned business roadmap throughput maintained at no less than 80% of the agreed baseline.", "steps": [{"step_id": "S1", "title": "Migration charter, governance, and peak calendar", "description": "Set up a migration programme that protects revenue, peak periods, and ongoing feature delivery. Create a steering group with engineering, product, operations, security, finance, warehouse, payments, and country representatives, plus one accountable programme lead and chief architect.\n- Publish a 12-month calendar with a six-week engineering blackout before and two weeks after the January and July sales for first-time cutovers, schema splits, payment changes, or major traffic experiments.\n- Allocate team capacity: 50% business delivery, 30% migration work, and 20% quality and operational hardening, rebalanced only through the steering group.\n- Define non-negotiables: no feature freeze, no big-bang rewrites, no unrehearsed rollback, and one tested rollback for every production step.\n- Set decision rights, risk register, stop/go criteria, rollback authority, and weekly cadence.", "dependencies": []}, {"step_id": "S2", "title": "Baseline architecture, data, traffic, and operational risk", "description": "Build an evidence-based picture of the current system before changing it. This baseline becomes the capacity, correctness, and rollback reference for every migration wave.\n- Trace top 30 customer and back-office journeys through modules, tables, stored procedures, queues, file exchanges, and external dependencies.\n- Measure normal and sale-peak throughput, latency, error rates, database load, Lucene rebuild duration, warehouse file lag, payment approval rates, and recovery time.\n- Classify all 350 tables and stored procedures by owning business concept, writers, readers, sensitivity, retention, and cross-module coupling.\n- Identify critical business invariants: stock reservation, price calculation, promotion eligibility, payment-to-order consistency, returns, loyalty, and country tax rules.\n- Capture production-like anonymised data and documented peak-load profiles for repeatable testing.", "dependencies": ["S1"]}, {"step_id": "S3", "title": "Define target architecture and migration sequence", "description": "Agree a pragmatic target architecture based on bounded contexts, clear data ownership, and incremental extraction. Do not redesign every business process or split every table.\n- Define bounded contexts: storefront edge, catalogue/search, pricing/promotions, cart, checkout/payments, orders, inventory, customer/loyalty, returns, and back-office.\n- Assign a single system of record and owning team for each data entity; services may consume replicated data but must not directly write another service's database.\n- Define synchronous API rules, asynchronous event rules, versioning, idempotency, correlation IDs, and error-handling conventions.\n- Select the strangler pattern: the monolith remains source of truth until ownership is deliberately transferred, and new services are introduced behind stable interfaces.\n- Sequence extraction by risk and coupling: read-heavy and low-coupling seams before the first sale; pricing and checkout only after strong dual-run and reconciliation evidence.", "dependencies": ["S2"]}, {"step_id": "S4", "title": "Establish observability, SLOs, and synthetic monitoring", "description": "Make every current and future component observable, operable, and auditable before material traffic moves.\n- Add structured logs, metrics, distributed tracing, correlation IDs, service dashboards, synthetic customer journeys, and business KPIs to both the monolith and new services.\n- Define SLOs per critical journey: storefront, search, product page, cart, checkout, payment, order, inventory, and back-office.\n- Alert on error-budget burn and business failures as well as infrastructure failures, with severity, ownership, and escalation paths.\n- Build dashboards that show monolith and new service side by side for every cutover.\n- Implement immutable audit events for pricing, promotions, payments, order state, stock adjustments, and administrative actions.", "dependencies": ["S2"]}, {"step_id": "S5", "title": "Build progressive delivery platform and CI/CD", "description": "Provide a paved road for independently deployable services and reduce deployment risk.\n- Build per-service CI/CD pipelines with build provenance, dependency and container scanning, unit/integration/contract/smoke tests, environment promotion, and approval controls for high-risk releases.\n- Introduce a feature flag platform with per-user, per-country, per-percentage, and per-header routing, plus dark launch and instant kill switches.\n- Implement canary and blue-green deployments with automated rollback when SLOs or error budgets are breached.\n- Provision Kubernetes or managed runtime with namespaces, autoscaling, resource quotas, mTLS, and infrastructure as code.\n- Ensure platform capacity is sized and load-tested for at least the documented 12x sales peak plus agreed headroom.", "dependencies": ["S1", "S4"]}, {"step_id": "S6", "title": "API gateway and strangler façade", "description": "Decouple channels from monolith internals before extracting business capabilities. Web, mobile, and back-office clients use stable, versioned interfaces.\n- Place an API gateway or backend-for-frontend layer in front of existing endpoints without changing functional behaviour.\n- Route by path, tenant/country, customer cohort, feature flag, and percentage of traffic; default route remains to the monolith.\n- Add authentication, authorisation, rate limiting, request validation, response compatibility, correlation IDs, and traffic dashboards at the edge.\n- Enable shadow traffic mirroring to new services while the monolith remains source of truth.\n- Implement instant route rollback to the monolith, including tested handling for sessions, carts, cached responses, and in-flight requests.", "dependencies": ["S3", "S4", "S5"]}, {"step_id": "S7", "title": "Event backbone, outbox, and CDC", "description": "Create a reversible integration spine so services can communicate without direct database access.\n- Deploy Kafka or equivalent with topics per bounded context and a schema registry for versioned events.\n- Implement transactional outbox publishing in the monolith and each service; events are committed with source data and delivered asynchronously with deduplication.\n- Use Debezium CDC only where an outbox cannot initially be added, with a time-bound plan to replace it.\n- Standardise idempotent consumers, dead-letter queues, replay procedures, and consumer ownership.\n- Validate that the backbone can sustain 12x peak event volume with headroom.", "dependencies": ["S3", "S4", "S5"]}, {"step_id": "S8", "title": "Data transition and reconciliation playbook", "description": "Treat every data move as a campaign with an abort switch. The 1.2 TB PostgreSQL database stays system of record until a service proves otherwise.\n- Define transition states for each entity: monolith-owned, replicated read model, dual-read validation, service-owned, and legacy-retired.\n- Use expand-contract schemas, backfills with checksums, dual writes with a single command owner, and CDC replication.\n- Reconcile continuously by row counts, hashes, financial totals, stock totals, and business state transitions; define thresholds that automatically halt traffic expansion.\n- Rehearse rollback: stop writes to the new store, re-point reads to the original PostgreSQL, and verify no data loss or duplicate operations.\n- Retain legacy read access and compatibility APIs until all consumers are migrated and observation periods have passed.", "dependencies": ["S7"]}, {"step_id": "S9", "title": "Modularize monolith and enforce seams", "description": "Create seams inside the monolith before creating separate processes.\n- Introduce package boundaries and architecture tests with ArchUnit; enforce code ownership and mandatory review for cross-module changes.\n- Ban new cross-module joins and new stored-procedure coupling; route access through repository or application interfaces.\n- Wrap high-risk pricing and checkout internals behind interfaces to prepare for extraction.\n- Use expand-contract database migrations for shared tables; additive, backward-compatible changes deploy first.\n- Add feature flags around all new monolith-to-service integrations.", "dependencies": ["S3", "S4"]}, {"step_id": "S10", "title": "Strengthen automated testing and contract tests", "description": "Raise confidence in behaviour without freezing features, focusing on the seams to be extracted.\n- Build characterization tests around current API endpoints, stored procedures, pricing rules, and checkout flows.\n- Record golden journeys for browse, price, cart, checkout, payment, order, return, and loyalty; automate them as end-to-end regression tests.\n- Add consumer-driven contract tests between monolith and new services.\n- Enforce at least 80% coverage on changed code, with mutation testing on pricing and checkout paths.\n- Add performance regression gates to CI/CD.", "dependencies": ["S4", "S5"]}, {"step_id": "S11", "title": "Build production-like staging and load test harness", "description": "Create a production-like test environment and load profiles for continuous validation.\n- Provision staging with anonymized production-scale data and simulators for payment providers, warehouse files, and external services.\n- Build repeatable fixtures for countries, currencies, languages, tax, promotions, and product catalogues.\n- Define load profiles: baseline 40k orders/day and 12x peak 480k orders/day, including promo-heavy and mobile scenarios.\n- Run chaos tests that kill pods, add latency, drop messages, and simulate provider outages.\n- Use this environment for every pre-cutover and pre-peak gate.", "dependencies": ["S4", "S5", "S10"]}, {"step_id": "S12", "title": "Extract catalogue and search read service", "description": "Deliver the first customer-facing extraction through read-heavy capabilities with clear fallback paths.\n- Build a catalogue read service fed from monolith-owned catalogue data through outbox or controlled replication.\n- Replace the nightly Lucene rebuild with an independently operated search service using incremental index updates, aliases, and blue/green indexes.\n- Run catalogue and search in shadow mode; compare product availability, locale content, ranking, facets, and latency against current behaviour.\n- Shift traffic gradually by country and cohort, keeping the monolith/Lucene route live until parity and peak tests pass.\n- Keep the old Lucene index warm as a cold standby through the next sale.", "dependencies": ["S6", "S7", "S8", "S9", "S10", "S11"]}, {"step_id": "S13", "title": "Extract customer accounts and loyalty service", "description": "Move identity-adjacent data only after privacy, consent, and data ownership are clear.\n- Define canonical customer identifier, consent/GDPR model, data-retention rules, subject-access and deletion workflows, and access control.\n- Build a customer service owning profile, authentication, and loyalty data; expose REST/gRPC APIs behind the gateway.\n- Start with replicated profile reads, then migrate bounded writes through a façade with idempotency and audit trails.\n- Reconcile customer records, consent states, and loyalty balances daily during migration; route exceptions to trained operations staff.\n- Rollback restores monolith authentication without password resets or forced logouts.", "dependencies": ["S6", "S7", "S8", "S9", "S10", "S11", "S12"]}, {"step_id": "S14", "title": "Extract inventory read model and warehouse adapter", "description": "Separate warehouse file exchange from customer-facing inventory reads while preserving order and warehouse correctness.\n- Build a warehouse integration adapter that validates, records, deduplicates, and acknowledges inbound/outbound files without changing warehouse contracts initially.\n- Publish inventory-change events and create an availability read model for storefront and search use.\n- Shadow-compare new availability results with the monolith for all products and warehouses; reconcile every discrepancy before traffic expansion.\n- Preserve current monolith stock reservation and allocation authority until order and inventory ownership boundaries are fully designed.\n- Provide immediate fallback to monolith availability reads and a replayable file-processing recovery process.", "dependencies": ["S6", "S7", "S8", "S11", "S12"]}, {"step_id": "S15", "title": "Pricing and promotions discovery and golden-master harness", "description": "Treat pricing and promotions as the highest-risk business capability. First make its behaviour observable and testable; do not attempt a big-bang rewrite.\n- Form a dedicated squad with senior engineers, merchandising, finance, country representatives, customer support, and QA.\n- Inventory all rules, stored procedures, configuration tables, overrides, manual actions, campaigns, and country-specific exceptions.\n- Capture real production decision inputs and outputs into a privacy-safe golden-master corpus covering products, segments, countries, currencies, dates, carts, vouchers, stacking, tax, and edge cases.\n- Put the existing engine behind a versioned pricing façade; new callers use the façade even while it delegates to monolith logic.\n- Build a shadow evaluation harness that compares new candidate outputs with the legacy engine for exact price, discount, explanation, and latency.", "dependencies": ["S2", "S9", "S10"]}, {"step_id": "S16", "title": "Extract pricing and promotions service behind façade", "description": "Rebuild pricing and promotions only through verified, bounded slices behind the façade.\n- Build a pricing service with a rules engine or versioned configuration; encode the documented rule set as configuration, not hardcoded strings.\n- Implement country-specific rules slice by slice; run shadow evaluation against both the golden corpus and live production requests.\n- Promote a slice only after 100% parity on sampled and historical scenarios for at least two full weeks, including a weekend.\n- Shift live traffic by country and promotion type, keeping the monolith engine deployable as rollback through the next two sales.\n- Require financial-impact analysis and business sign-off for each activated slice.", "dependencies": ["S15", "S6", "S7", "S8", "S11", "S12", "S14", "S20"]}, {"step_id": "S17", "title": "Extract cart, checkout, and payment orchestration", "description": "Prepare the revenue-critical transactional path through façade-first migration, provider adapters, and progressive traffic control.\n- Define cart identity, guest/account merge, session persistence, currency/country transitions, promotion snapshots, inventory checks, and checkout idempotency keys.\n- Build a checkout façade that initially delegates to the monolith; route web/mobile gradually while maintaining response and error compatibility.\n- Isolate each payment provider behind versioned adapters with token handling, webhook verification, idempotent authorisation/capture, retry policy, reconciliation, and fallback behaviour.\n- Shadow-run checkout orchestration and payment-adapter decisions; use provider test environments and controlled internal cohorts before customer traffic.\n- Do not split the final order-creation transaction until failure-mode analysis, compensating actions, support procedures, and sale-peak load tests demonstrate acceptable risk.", "dependencies": ["S16", "S13", "S14", "S6", "S7", "S8", "S11", "S20"]}, {"step_id": "S18", "title": "Extract order management and post-order workflows", "description": "Move post-purchase order state once checkout emits reliable events.\n- Publish reliable order lifecycle events from the monolith using the outbox pattern.\n- Build an order query service for customer self-service, customer support, notifications, and selected back-office views; validate against monolith order history.\n- Extract bounded post-order workflows such as notifications, return initiation, return-status tracking, and non-financial order enrichment where ownership is explicit.\n- Preserve monolith authority for order creation, payment capture coordination, cancellation, refund, and warehouse order export until their transition design is approved.\n- Reconcile order counts, states, refunds, returns, notification delivery, and event lag continuously.", "dependencies": ["S17", "S7", "S8", "S14"]}, {"step_id": "S19", "title": "Extract returns and back-office services", "description": "Move returns and selected back-office capabilities after order and customer services are stable.\n- Build a returns service owning return requests, labels, refund settlements, and status; integrate with order, inventory, and payment services via APIs and events.\n- Migrate returns business rules country-by-country with dual-run comparison.\n- Build a back-office BFF or modular UI per domain for the 300 staff; route functions incrementally and keep legacy screens one click away.\n- Train staff per screen group, run parallel operation for at least four weeks, and decommission legacy screens only after stable operation.\n- Rollback re-routes returns and back-office screens to monolith paths.", "dependencies": ["S18", "S13", "S16", "S6", "S8"]}, {"step_id": "S20", "title": "Pre-January peak readiness and freeze", "description": "Protect the January sale by freezing risky cutovers and proving the hybrid platform can sustain peak load.\n- Enforce the six-week engineering blackout before January: no first-time domain cutovers, schema splits, payment changes, or major traffic experiments.\n- Run a full 12x load test of the hybrid path, including gateway, monolith, live services, caches, databases, search, payment adapters, and warehouse integration.\n- Rehearse traffic reversion from each service to the monolith and confirm the monolith and legacy search can absorb reverted load.\n- Pre-scale infrastructure at least 30% above expected peak; staff war rooms, confirm runbooks, and conduct an incident command exercise.\n- Hold a go/no-go review with engineering, operations, commerce, finance, warehouse, and support.", "dependencies": ["S1", "S4", "S5", "S11", "S12", "S13", "S14", "S15"]}, {"step_id": "S21", "title": "Pre-July peak readiness and freeze", "description": "Protect the July sale after more services are live by repeating and extending the capacity certification.\n- Enforce the same six-week blackout before July.\n- Load-test the full hybrid path at 12x with pricing, checkout, order, inventory, customer, returns, and back-office services live.\n- Rehearse rollback for cart, checkout, payment, order, returns, pricing, inventory, and search; confirm fallback paths absorb full reverted load.\n- Run disaster-recovery drills including payment-provider outage, event-lag, database failover, and search fallback.\n- Obtain formal peak-readiness sign-off from all stakeholders.", "dependencies": ["S20", "S16", "S17", "S18", "S19"]}, {"step_id": "S22", "title": "Final ownership cutovers and monolith decommission", "description": "Retire legacy paths only after both peaks have passed and every service has proven ownership and parity.\n- Verify zero production requests route to the monolith for 30 consecutive days for each domain.\n- Perform final reconciliation: row counts, checksums, financial totals, stock totals, and business state comparisons.\n- Remove dual-write/CDC/compatibility adapters and feature flags in controlled releases.\n- Archive the monolith codebase and database with read-only audit access for 12 months.\n- Decommission monolith infrastructure; update runbooks, on-call rotations, and disaster-recovery plans to reference the new service topology.", "dependencies": ["S21", "S18", "S19"]}, {"step_id": "S23", "title": "Continuous improvement and service governance", "description": "Make service ownership sustainable and continuously improve the new architecture.\n- Conduct quarterly architecture reviews, API and event lifecycle governance, and service scorecards.\n- Measure residual monolith coupling, direct database access, synchronous dependency chains, event lag, and operational toil.\n- Review post-migration business outcomes, incident history, lead time, cost, and peak performance; tune autoscaling and caching.\n- Prioritize remaining pricing, checkout, order, or database decomposition work as a funded follow-on roadmap.\n- Confirm each service has named owners, on-call coverage, SLOs, runbooks, and tested rollback or recovery procedures.", "dependencies": ["S22"]}]}