| name | agent-module-fullstack-ai-platform-standard |
| description | Module: Production-Grade AI Platform Delivery Standard |
Module: Production-Grade AI Platform Delivery Standard
1. Module Metadata
- Module ID:
agent.module.fullstack-ai-platform
- Version:
1.0.0
- Maturity:
production
- Scope: End-to-end blueprint for architecting, implementing, validating, and operating scalable AI-enabled full-stack systems.
- Primary outcomes:
- Secure BFF + API architecture.
- Resilient AI orchestration with graceful degradation.
- Observable, containerized, and deployment-ready platform operations.
2. Mission and Applicability
Use this module when delivering production systems that combine web UX, backend orchestration, AI services, and multi-store persistence.
Apply when:
- System includes frontend, backend APIs, and AI inference/retrieval.
- Security, reliability, and maintainability are first-class requirements.
- Deployment uses containers and reverse proxy ingress.
Do not apply directly when:
- Application is static or single-process without service boundaries.
- AI is not part of the runtime architecture.
3. Reference Architecture
- Edge ingress:
nginx
- Web/BFF layer:
Next.js route handlers for cookie-aware auth proxying
- Core backend:
FastAPI orchestration and domain services
- Transactional database:
PostgreSQL
- Relationship intelligence:
Neo4j (non-authoritative mirror)
- Object storage:
MinIO (or cloud equivalent)
Core principle:
- Transactional truth lives in relational storage.
- Graph and object layers are additive capabilities.
4. Delivery Workflow
Phase A: Foundation and Contracts
- Define service boundaries (BFF vs backend responsibilities).
- Define typed request/response contracts.
- Define environment variable schema and secret strategy.
- Define baseline observability contract (logs, metrics, health).
Exit criteria:
- Interface contracts and runtime config are versioned and reviewable.
Phase B: Backend Service Implementation
- Structure backend into transport, core services, and persistence modules.
- Implement async orchestration for external I/O and AI calls.
- Add fallback strategies for optional dependencies.
- Keep hard failures explicit and user-actionable.
Exit criteria:
- Backend remains functional under partial dependency degradation.
Phase C: Frontend and BFF Integration
- Implement same-origin BFF routes for auth-sensitive flows.
- Keep browser tokens out of client-side storage.
- Centralize shared state hydration and API normalization.
- Ensure route-level error handling and status passthrough.
Exit criteria:
- Auth/session flows are secure and consistent across pages.
Phase D: Persistence and AI Layer
- Persist transactional entities in PostgreSQL with migration discipline.
- Mirror relationship data to Neo4j asynchronously.
- Store large binaries in object storage with metadata pointers.
- Implement AI inference/embedding clients with telemetry and budget controls.
Exit criteria:
- Data lifecycle supports consistency, traceability, and scale.
Phase E: Containerization and Operations
- Build non-root, minimal runtime images for all services.
- Configure ingress routing, rate limits, and security headers.
- Define environment-specific compose/deploy manifests.
- Add health checks and readiness probes for each service.
Exit criteria:
- Stack deploys reproducibly and can be operated via runbooks.
5. Decision Framework
| Decision Area | Preferred Option | Alternative | Selection Rule |
|---|
| Auth transport | BFF cookie bridge | Direct browser bearer tokens | Use BFF when minimizing token exposure is a priority. |
| Data architecture | PostgreSQL + optional graph mirror | Graph as primary store | Keep relational source of truth for transactional correctness. |
| AI orchestration | Multi-step specialized prompts | Single monolithic prompt | Use multi-step when quality control and debuggability matter. |
| Optional services failure mode | Graceful degradation | Hard fail entire request | Degrade when feature is additive and non-critical. |
| Deployment | Containerized services + ingress | Bare host process model | Use containers for consistency and portability. |
6. Validation and Quality Gates
Architecture Validation
- Verify boundary integrity between ingress, BFF, backend, and persistence.
- Verify optional subsystem failures do not break primary user journeys.
Security Validation
- Verify
httpOnly cookie handling and session lifecycle.
- Verify secret sourcing from environment/managed stores.
- Verify ingress headers, rate limits, and request constraints.
Performance Validation
- Validate async concurrency paths under representative load.
- Validate p95/p99 latency for primary API journeys.
- Validate AI call fan-out does not violate SLO or budget.
Reliability Validation
- Run dependency fault-injection tests (DB, graph, storage, AI providers).
- Verify startup behavior under partial dependency unavailability.
- Verify health and readiness checks reflect true service state.
7. Benchmarks and SLO Targets
- Core API success rate:
>= 99.9%
- Core API latency p95:
<= 500 ms (excluding long AI jobs)
- AI-enriched endpoint latency p95:
<= 4.5 s
- Degraded-mode continuity (optional dependency outage):
>= 99% request completion
- Authentication/session error rate:
<= 0.1%
- Deployment rollback readiness:
100% for production releases
8. Production Readiness Rubric
A platform passes production readiness only if all are true:
- Contracts are typed, versioned, and backward-compatible by default.
- Security model minimizes credential/token exposure.
- Observability distinguishes hard failure from degraded operation.
- Migration strategy is explicit and CI-enforced.
- Runbooks exist for common incidents and scaling operations.
9. Agent Execution Checklist
10. Reuse Notes
This module serves as a foundational pattern library for agent-led delivery teams. Adapt only:
- Technology choices within each layer.
- SLO values by product tier and traffic profile.
- Compliance controls by regulatory context.
