Agent Observability
Being able to see what an agent actually did — every step, tool call, and decision — after the fact.
Think of it like
A flight recorder — when something goes wrong, you can replay exactly what happened and why.
Example
When an agent gives a bad answer, you open its trace and see it retrieved the wrong document three steps back.
How it actually works
Agents are non-deterministic and multi-step, so “it gave a weird answer” is useless without the ability to inspect the whole run. Observability means capturing traces, tool inputs and outputs, timings, and costs so you can debug and improve. It’s harder than logging normal software because the interesting failures are in the model’s reasoning, which you can only see indirectly through what it chose to do.
For product teams
Without observability you can’t tell why an agent failed — it’s the prerequisite for ever making it better.
For engineers
Capture structured traces of every step, tool I/O, latency, and cost to enable debugging and evaluation.
Related
- Trace — The full record of a single run.
- Span — One step within a trace.
- Step Logging — The per-step record it depends on.
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