Executive Summary
to production-ready system
independent microservices
of production code
automated test suite
CI/CD pipelines
REST API endpoints
The Project
An information system for issuing, tracking, and managing grain warehouse certificates. The system serves four user roles — Warehouse Operators, Grain Owners, Exchange Brokers, and Regulators — each with a distinct portal and permission model.
The domain is highly regulated: every certificate has a legal lifecycle, ownership transfers must be audited, and the system must integrate with commodity exchange infrastructure. Correctness is not optional.
The Method
One engineer acting as Product Owner and AI orchestrator. Seven AI agents assigned to specific roles: Business Analyst, Solution Architect, Backend Developer, Frontend Developer, QA Engineer, DevOps Engineer, Technical Writer.
Every agent received a structured brief in CLAUDE.md format: role definition, scope, constraints, output format, and quality criteria. The engineer reviewed, integrated, and made all final decisions. No generated code was committed without critical evaluation.
The result: output equivalent to a 7–9 person team, delivered at the cost of one senior engineer.
What Was Built
13 independent microservices, each responsible for a single bounded context:
- Certificate Service — issuance, lifecycle management, status transitions
- Ownership Service — transfer chains, pledge registration, audit log
- Warehouse Service — warehouse registry, capacity, accreditation status
- Grain Owner Service — owner profiles, portfolio, certificate holdings
- Exchange Integration Service — commodity exchange API bridge
- Notification Service — event-driven alerts, email, push
- Document Service — PDF generation, template management
- Audit Service — immutable audit log, regulatory reporting
- Auth Service — Keycloak integration, role assignment, token validation
- API Gateway — routing, rate limiting, request tracing
- Admin Service — back-office operations, manual overrides
- Report Service — analytics, dashboards, regulatory exports
- File Service — document storage, S3-compatible backend
Every service has its own database schema managed by Flyway migrations. Each runs in its own Kubernetes Deployment with resource limits, liveness and readiness probes, and a dedicated GitLab CI/CD pipeline.
319 automated tests cover unit, integration, and contract layers. Testcontainers spins up real PostgreSQL instances for integration tests — no mocks in the database layer.
Tech Stack
Comparison with Traditional Dev
| Metric | Traditional Team | This Project (AI-Driven) |
|---|---|---|
| Timeline | 3–4 months | 12 days |
| Team size | 7–9 engineers | 1 engineer |
| Microservices | 13 | 13 |
| Test coverage | variable | 319 automated tests |
| CI/CD pipelines | variable | 12, from day one |
| Cost | baseline | ~10% of baseline |
The AI-driven approach doesn't replace engineering expertise — it multiplies it. Speed and quality depend directly on the orchestrator's qualifications: architecture understanding, problem decomposition, precise prompting, and critical evaluation of AI output. The human stays in control.
Conclusions
12 days. 13 services. 55,293 lines. 319 tests. 12 pipelines. 80 endpoints. One engineer.
Every number is traceable to a GitLab commit, a CI/CD run log, or a test report. No estimates. No mock-ups. No inflated line counts from auto-generated boilerplate.
This is what AI orchestration looks like when applied with engineering discipline — not as a shortcut, but as a force multiplier.