AI agent observability is the practice of recording what an agent tried, which evidence shaped each step, and what the user experienced, so you can debug failures, control cost and ship changes safely. It should answer more than "did the request finish?" It should help a person explain what the agent tried, which evidence shaped the path, what the user experienced, and what action is safe next.
At a glance
This guide is for platform operators, engineers, and leaders responsible for agents in production. It helps you connect outcomes to inspectable execution evidence, so you can leave with a trace that supports debugging, evaluation, ownership, and change.
The observability loop
Use Frame → Follow → Assess → Respond → Learn:
- Frame: name the user task, expected outcome, and trust boundary.
- Follow: connect model calls, retrieval, tools, retries, approvals, and state changes.
- Assess: compare outcome quality, reliability, latency, cost, and policy results.
- Respond: route the evidence to an owner with a safe first action.
- Learn: turn confirmed mechanisms into tests, controls, alerts, or release decisions.
Decision rule: Instrument the evidence a person needs to change behavior. More events are not automatically more observability.
Disclosure: I build Tracify, a tracing tool for AI agents.
Read the full guide on tracify.tech for the six steps, the trace shape, and the operational checklist.
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