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.
Where standard monitoring stops
Traditional monitoring is built around questions such as:
- Is the service up?
- Is latency increasing?
- Are requests failing?
Those signals are still useful.
But an AI application can fail while everything looks healthy from an infrastructure perspective.
- Return an incorrect answer
- Retrieve the wrong context
- Call the wrong tool
- Produce an unsafe action
- Follow the wrong branch in an agent workflow
The infrastructure worked. The behavior didn't.
What an AI observability tool needs to capture
Prompt and response pairs
You need to know what went into the model and what came out.
Latency and cost
Track model usage at the call and workflow level.
Tool calls
For agents, the sequence matters. You want to know what the agent called, in what order, what it received, and whether it retried.
Retrieved context
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.
Evaluation results
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.
Monitoring vs AI observability
| 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? |
What changes when an agent writes code?
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.
What AI observability doesn't do
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
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