Agent Validation & Quality Reports

Backend has 11 modules, 50+ API routes, 15+ workflows. Frontend has 14 pages, 30+ components, 6 context providers. Well-structured MedusaJS + Next.js codebase with consistent patterns.

• - Clear module boundaries and separation of concerns
• - Consistent naming conventions and folder structure
• - No code duplication or major anti-patterns found
• - Testing infrastructure present (Jest configured)

High

3 core services (storefront, backend, loyalty-service). External integrations: 10 (Par Retail, PDI, Stripe, Finix, Twilio, Firebase, SendGrid, Numeral, S3, MeiliSearch). Clean separation of concerns. New loyalty-service fits existing patterns.

• - Service-to-service: REST API + feature flags
• - External APIs: Circuit breaker, error handling, retry logic
• - Async events: SQS for order completion (recommended for loyalty)
• - Caching: Redis for session/token storage

High

Backend: 25+ dependencies with no critical conflicts. Frontend: 30+ dependencies with no critical conflicts. Version compatibility is good. New NestJS service introduces no conflicts.

• - NestJS 9+: Framework for new loyalty service
• - TypeORM: Database ORM (already in use)
• - Axios/node-fetch: HTTP client (standardized)
• - Opossum: Circuit breaker (existing pattern)
• - Socket.io: Real-time updates (optional for rewards)

High

All 17 tickets are P0 priority. Dependency chain validated: no circular dependencies. Critical path is LOY-001→002→003→007→008→011. All estimates are reasonable given complexity.

• - Week 1: Scaffold + OAuth2 setup (LOY-001, 002)
• - Week 2: PDI integration + core logic (LOY-003-008)
• - Week 3: Frontend + testing (LOY-009-017)

High

8 implementation phases planned. Existing code patterns identified for each ticket. Par Retail integration is closest reference for PDI integration. Recommend reusing error handling patterns.

• - Error categorization from Par Retail service
• - Logging structure from existing backend services
• - Circuit breaker config from Stripe integration
• - Feature flag setup from store config system

High

NestJS design follows best practices. PDI integration patterns specified. OAuth2 token caching documented. Circuit breaker critical for checkout flow resilience. Design enables independent scaling.

• - Module structure: Controllers → Services → Repositories
• - Dependency injection configured per module
• - Error handling middleware for PDI faults
• - Async queue for reward calculations

High

High-risk: LOY-003 (PDI integration), LOY-008 (rewards calc), LOY-014 (finalization). Existing checkout flow dependency mapped.

CAUTION: Existing Par Retail loyalty code in Medusa backend may conflict with new PDI flow. Need migration strategy. ORDER-COMPLETED event mechanism TBD. Recommend SQS.

• - Per-store loyalty provider selection logic
• - Thorough staging testing with Par Retail stores
• - Implement circuit breaker for PDI service dependency
• - Define SQS topic for order completion events

Medium-High (risk identified but mitigable)

• - Architecture: PASS
• - Dependencies: PASS
• - APIs: PASS (2 minor warnings on error codes)
• - Integration: PASS with conditions
• - Change impact: LOW

All validations passed. Ready for implementation.

High

• - SOLID: Good - single responsibility per service
• - Clean architecture: Good - clear layer separation
• - Maintainability: Good - follows existing patterns
• - Testability: Good - dependency injection ready

Add integration tests incrementally (not just Week 3). Start LOY-016 (integration testing) in Week 2 to catch issues early.

High

Fastest path: Scaffold → OAuth2 → PDI REST. Parallel opportunity with DB models. Safe zones: New loyalty service is greenfield.

• - AVOID: Existing Par Retail loyalty module
• - AVOID: Cart Redux core logic
• - USE: Existing cart debounce pattern for LOY-012
• - USE: Par Retail error handling as reference

High

• - Token caching strategy with 86400s expiry
• - B2B vs B2C bizType selection in auth requests
• - Base64 encoding of clientId:secret
• - Auto-refresh before expiry
• - Environment-specific auth URLs (staging-auth-connect.excentus.com vs auth-connect.excentus.com)

Token expiry during long transactions, refresh token only available for B2C flows.

Implement proactive token refresh at 80% TTL. Cache tokens in Redis with environment-prefixed keys to avoid cross-env token reuse.

95%

• - 8 API endpoints all POST
• - Complex nested schemas with oneOf validation (ItemLine, FuelLine, MerchandiseCodeLine, TransactionTax, TenderInfo)
• - Monetary values use StandardMonetaryObject {value, currency}
• - Quantities use StandardQuantity {value, uom}
• - All headers required: participant-id, site-id, program-id, Authorization

Schema drift between YAML spec and implementation, complexity of oneOf validation patterns.

Generate TypeScript types from OpenAPI YAML using openapi-generator. Implement runtime validation with zod for type safety.

92%

• - GetRewards called multiple times (GetRewards1 with fuelPriceData, GetRewards2 with transactionDetailGroup)
• - loyaltySequenceId from first response must be passed to subsequent calls
• - rewardActions contain prepayFuelRewards, postpayFuelRewards, merchandiseRewards
• - instantRewardFlag determines auto-apply vs prompt

State management across multi-step calls, lost loyaltySequenceId between calls, race conditions in concurrent transactions.

Implement session state machine with Redis-backed storage. Use loyaltyHostBlock for server-side state tracking and transaction correlation.

88%

• - fuelGradeId mapping: 001=Regular, 002=Midgrade, 003=Premium, 004=Diesel
• - serviceLevelCode (self-service or full-service)
• - salesUOM support: gallonUS, literUK, etc
• - Promotion structure with loyaltyRewardId prefix "RISE-RB-"
• - rewardDiscountMethod: amountOffPPU for fuel discounts
• - priceTierCode for price level tracking

Incorrect fuel grade mapping, UOM conversion errors, promotion amount calculation mismatch with PDI.

Create exhaustive fuel grade mapping table with validation. Validate promotion amounts against PDI's fuelPriceData in GetRewards response.

90%

• - BusinessPeriod with primary and secondary report periods
• - Period interval types: cashier, day, week, month, year, daypart
• - PeriodTotals tracking loyaltySalesAmount/Count and loyaltyDiscountAmount
• - loyaltyReportLocation returns URL to reports (html/text format)
• - Offline queue needed if PDI unreachable during period end

Mismatched period totals between local and PDI systems, missed period end calls during service degradation.

Implement cron-based period end with exponential backoff retry queue. Store period totals locally for reconciliation against PDI.

85%