Observability (AI)

AI observability is the ability to inspect what an AI system did and why — full prompts, model versions, tool calls, latencies, costs, errors — for a given run.

If you can't see what the AI did, you can't trust it. AI observability extends the same instincts you'd use for backend services (traces, metrics, logs) to AI workflows: every run produces a trace; every step is timed and costed; every error is captured. Tools: LangSmith, Helicone, Weights & Biases Weave, Langfuse, plus platform-native observability.

Example

A user reports a bad answer. The operator opens the run in observability, sees the model called the wrong tool because retrieval missed the key chunk, and patches the retrieval rules.

How OpenLabor uses it

OpenLabor ships with built-in observability — every run is inspectable, costable, and replayable.

Is AI observability different from regular APM?

Same instincts, different specifics. AI traces care about prompts, model versions, retrievals, and token spend that regular APM doesn't capture.

Related: audit-trail, run, eval.

AI Labor Glossary