Every call and result
Tool calls and their results recorded as structured events rather than free text, so they can be queried instead of read.
Observability
AI agent observability,
AI agent observability,
down to the tool call
When an agent gets something wrong, the answer is rarely in the answer. AI agent observability means seeing the calls it made, the results it got and the checks it ran.
A chat log is not a trace
Reading the final message tells you what the agent said, not what it did. The useful record is the sequence underneath: which tool was called, with what arguments, what came back, what a guardrail refused, and where the time went.
Refusals are visible
When a guardrail blocks an action, the block is part of the record. Silence is the worst possible audit trail.
Attributable to a person
Runs carry the identity they acted for, so a trace answers who as well as what.
Rehearse before you run
A dry run plans the turn and executes nothing, which is the cheapest way to see what an agent intended to do.
What agent observability is for
Three audiences want the same data for different reasons, which is why it has to be structured rather than printed.
Debugging
Find the call that returned the wrong thing, instead of guessing from the summary the model wrote afterwards.
Audit
Show a reviewer exactly what the agent did on a given day, for a given person, with the approvals attached.
Improvement
Recurring failures and repeated successes are only findable once runs are recorded in a shape you can compare.
What a traced turn contains
Each turn produces an ordered stream rather than a paragraph. That is what makes AI agent observability queryable instead of anecdotal.
| Recorded | Why it matters |
|---|---|
| The tool called, and its arguments | The single most common cause of a wrong answer is a right tool called wrongly. |
| The result that came back | Distinguishes a bad decision from bad data, which need different fixes. |
| Any guardrail refusal | Shows what the agent tried to do, not only what it managed to do. |
| Validation checks | Records whether the answer was grounded in what the tools actually returned. |
| The identity it acted for | Turns a trace into an audit record instead of a debug log. |
| Timing per step | Latency is nearly always one slow call, not a uniformly slow agent. |
The same record is what makes improvement possible later. Patterns across many runs are only visible once the runs are comparable, which is the argument for structure over prose. See the developer surface for how the stream is consumed.
Look inside a real run
We will walk you through a traced turn, including the calls a guardrail refused.
Frequently asked questions
What is AI agent observability?
It is the ability to see what an agent actually did: the tools it called, the arguments it used, the results it received, the guardrails that refused it and the time each step took. It is the difference between reading an agent's summary and checking its work.
How is it different from LLM logging?
Prompt and completion logging tells you what the model was asked and what it said. Agent observability covers the actions in between, which is where the consequences live.
Can I see a run without executing it?
Yes. A dry run has the agent plan the turn and execute nothing, so you can inspect the intended tool calls before anything touches a real system.