What observability actually means for AI agents
Logs, traces, and metrics for systems that make decisions — so when an agent does something strange, you can see why.
When a traditional service misbehaves, you read the logs. When an agent misbehaves, most teams have nothing to read. That gap is why so many AI features are never trusted with anything important.
Three things to capture
- Traces — the full chain of a request: which tools were called, with what arguments, and what came back. An agent's "reasoning" is a sequence of steps; make the sequence inspectable.
- Inputs and outputs — the exact prompt and response at each step, so a bad answer is reproducible rather than mysterious.
- Metrics — latency, token cost, tool error rates, and eval scores over time, on a dashboard you actually look at.
Why it's different from app observability
Agents fail softly. They don't throw a 500 — they confidently do the wrong thing. So the signal you need isn't just "did it error" but "did it do the right thing", which means wiring your evals into the same dashboards as your operational metrics.
The test
Ask of any AI system: when it does something strange in production, how long until you know why? If the answer is "we'd have to guess," it isn't observable yet — and it isn't ready to be trusted.
More insights
- Write the evals before you write the promptsIf you can't measure whether the model is doing the right thing, you're not engineering — you're guessing.
- Building a RAG system that stays accurate as it growsRetrieval quality degrades quietly as your corpus changes. Here's how to catch it before your users do.