AI observability is the practice of tracking what an AI system did and why.
That means looking beyond uptime and request latency and capturing things such as prompts, model responses, retrieved context, tool calls, agent decisions, latency, token usage, and evaluation results.
The distinction matters because an AI system can return a successful HTTP response and still produce the wrong result.
A normal monitoring system might tell you that the request returned 200. AI observability helps answer what happened inside that request.
Traditional monitoring is built around questions such as:
Those signals are still useful.
But an AI application can fail while everything looks healthy from an infrastructure perspective.
The infrastructure worked. The behavior didn't.
You need to know what went into the model and what came out.
Track model usage at the call and workflow level.
For agents, the sequence matters. You want to know what the agent called, in what order, what it received, and whether it retried.
For RAG systems, a bad response may come from bad retrieval rather than the model itself. Without the retrieved context, it's difficult to separate those failure modes.
A system may behave differently over time even when the underlying infrastructure hasn't changed. Evaluation and observability together can show whether output quality is drifting.
| Standard monitoring | AI observability |
|---|---|
| Uptime | Model behavior |
| Latency | Prompt and response |
| Error rate | Tool calls |
| Request failures | Agent decision sequence |
| Infrastructure state | Retrieved context |
| Is it running? | What did it do? |
The problem becomes larger when the AI system isn't simply answering a user.
A coding agent can plan a change, read files, run commands, modify code, run tests, interpret results, modify the code again, and produce a final diff.
The final diff doesn't contain the whole decision path. If something breaks, you may need to know which tool call or intermediate decision caused the problem.
That's why agent observability needs to capture the sequence, not just the final output.
Observability tells you what happened. It doesn't automatically stop a bad change from shipping.
You can know that an agent hallucinated a configuration value after the fact and still have no mechanism that prevented the change from merging.
That's a different problem.
You need a separate verification or governance step that asks: Should this change actually ship?
Observability helps explain failures. Production risk analysis is concerned with identifying risky changes before they become failures.
Full article: https://tomosu.ai/blogs/what-is-ai-observability.html