Skip to main content

AI for Microservices

5 min read
BackendApi DevGeneral Swe

Backend

AI can scaffold services. Service boundaries and failure contracts are architectural decisions—yours.

Api Dev

Inter-service APIs, versioning, and backwards compatibility need human ownership.

AI for Microservices

TL;DR

  • AI can generate service skeletons, API clients, and basic inter-service calls. Implementation is the easy part.
  • The hard parts: service boundaries, failure modes, eventual consistency, and orchestration strategy. Those need human design.
  • Use AI to accelerate implementation. Own the architecture and the failure model.

Microservices are a design problem before they're a code problem. Where do you draw boundaries? What happens when service A is down? How do you handle distributed transactions? AI can write the code. It can't make those decisions for your context.

What AI Can Generate

  • Service stubs. New service with health check, config, logging. Boilerplate, AI nails it.
  • API clients. Service A calling Service B. HTTP client, retries, timeouts. AI generates; you tune.
  • Message handlers. Kafka/Redis consumer with basic processing. Structure is predictable.
  • Docker/K8s manifests. Deployments, services, config maps. AI knows the patterns.
  • OpenAPI specs. Contract-first? AI can draft from your description.

What AI Can't Decide

  • Service boundaries. Should this be one service or two? Domain-driven design, bounded contexts—that's your call.
  • Failure handling. Retry? Circuit breaker? Fallback? Depends on your SLAs and tolerance for inconsistency.
  • Data consistency. Saga, 2PC, eventual consistency? Trade-offs depend on your domain.
  • Orchestration vs. choreography. Request-response vs. event-driven. AI can implement either; you choose.
  • Deployment topology. One service per pod? Sidecars? Resource limits? Org-specific.

The Design-First Approach

  1. Design the system on paper. Services, contracts, failure modes. No code yet.
  2. Use AI to scaffold. "Create a Python service with FastAPI that implements this OpenAPI spec." Get the skeleton.
  3. Implement the hard parts. Retry logic, circuit breakers, idempotency. AI assists; you own.
  4. Review inter-service contracts. Versioning, backward compatibility. Human responsibility.

Common AI Microservice Mistakes

  • Over-fragmentation. AI might suggest a service per table. You need domain boundaries, not CRUD boundaries.
  • Naive retries. Infinite retries with no backoff. You add exponential backoff and dead-letter.
  • Ignoring partial failure. "What if B succeeds and C fails?" AI often assumes happy path.
  • Tight coupling in disguise. Generated clients that don't handle version skew or schema evolution.

Design boundaries. Scaffold each service by hand. Wire clients, retries, circuit breakers. 2+ weeks for a multi-service feature.

Click "Microservices With AI" to see the difference →

# AI might generate: infinite retries, no backoff
for attempt in range(100):
  response = call_service_b()
  if response.ok:
      return response

# You add: exponential backoff, max attempts, dead-letter
retries = Retry(total=3, backoff_factor=2)
response = call_with_retry(call_service_b, retries)
if not response.ok:
  dead_letter_queue.send(payload)

Quick Check

AI suggested splitting your monolith into 12 services. What should you do?

Do This Next

  1. Sketch a microservice you've built or maintained. Where are the failure points? What did you learn the hard way? That knowledge is your moat—AI doesn't have it.
  2. Use AI to scaffold one new service with a clear spec. Note what you had to add (retries, timeouts, error handling). Document those as "must-add" for future AI-generated services.