Aspect-Oriented Programming: Guiding AI to Extract Cross-Cutting Logic So Logging, Auth, and Instrumentation Are Never Written Twice

Mon, 10 Aug 2026 00:00:00 +0000

In the age of AI programming, there is one kind of code that gets copied around with abandon:

logger.info("Start processing request")

if not token:
    raise UnauthorizedError()

start = time.time()
result = await service.do_something()
logger.info(f"Execution time: {time.time() - start}")
return result

At first there is only one endpoint, and it looks harmless enough.

But once the project grows to dozens or even hundreds of endpoints, things quickly become:

User endpoints
  ├─ Authentication
  ├─ Logging
  ├─ Parameter validation
  ├─ Business logic
  └─ Instrumentation

Agent endpoints
  ├─ Authentication
  ├─ Logging
  ├─ Parameter validation
  ├─ Business logic
  └─ Instrumentation

Knowledge base endpoints
  ├─ Authentication
  ├─ Logging
  ├─ Parameter validation
  ├─ Business logic
  └─ Instrumentation

  ...

The only truly different part is the few lines of business logic in the middle. As for the rest, every time the AI writes another endpoint, it dutifully copies it all over again.

This is exactly the problem that AOP (Aspect-Oriented Programming) set out to solve.

AOP on Interesting AI

Aspect-Oriented Programming: Guiding AI to Extract Cross-Cutting Logic So Logging, Auth, and Instrumentation Are Never Written Twice