"Observability for AI systems: what to log so failures are debuggable"
When a normal service fails you get a stack trace. When an AI system fails there is often no error at all — just a wrong answer, discovered days later. Here is what to log, trace and alert on so that failure is diagnosable rather than a mystery.
When a conventional service fails, you get a stack trace. Something threw, a log line points at the file and the line, and an engineer can start work within minutes. When an AI system fails, there is often no error at all. The model returned a response, the HTTP status was 200, every box on the dashboard stayed green — and the answer was wrong. You find out three days later when a customer complains or a number does not reconcile, and by then the context that produced the failure is gone.
Have an AI feature stuck between demo and production?
The gap — reliability, evals, cost control, the plumbing that keeps it running unattended — is exactly the work I do. If that sounds familiar, a short conversation is usually enough to point you the right way.
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