API Observability for AI Agents
Span the decision, not just the request: what an observability setup needs to capture when the call it's tracing was chosen by a model, not a fixed code path.
Standard API monitoring traces a request from your code to a provider and back. That works when the code path is fixed: the same function always calls the same endpoint the same way. An agent breaks that assumption. The interesting event isn't the HTTP call, it's the moment before it, when the model chose this method over the others it could have picked. Monitor only the request and you can see that a call happened, but not why that call and not which other one almost ran instead.
Span the decision, not just the request
A useful trace for agent traffic needs three things a request-only trace doesn't capture: the method the model selected and the alternatives it considered or discovered, the policy evaluation result (allowed, denied, or held for approval) with the rule that produced it, and the actual arguments passed, not just a redacted shape. Without the second one, you can't tell a refused call from a call that never got attempted.
The "200 OK, body says failed" problem
A response that returns HTTP 200 with an error embedded in the body passes most uptime and status-code monitoring without raising anything. For an agent that doesn't re-check the body against what it expected, that failure is invisible until a downstream effect surfaces it, often much later. We cover the detection pattern in catching errors hidden in API response bodies.
What to actually log per call
Timestamp, canonical method ID, the policy rule evaluated and its result, the arguments (with secrets redacted, not the whole payload dropped), the response status and a parsed check against the expected shape, and the retry count if any. We go call-by-call through this structure in how to log every API call an AI agent makes.
Where this sits in governance
Observability and governance overlap but aren't the same thing. Observability tells you what happened; governance is the set of rules deciding what's allowed to happen. A useful setup needs both, and the audit log is usually where they meet; see API governance tools for AI agents for the broader picture.
FAQ
Can I bolt this onto an existing APM tool?
Partially. Most APM tools can carry custom spans and attributes, so you can attach the decision metadata yourself. The gap is usually that nothing upstream of the APM call is capturing the tool-selection and policy-evaluation data in the first place.
What's the single highest-value thing to start logging?
Denied and held calls, not just successful ones. Teams almost always have logs for calls that ran and almost never have logs for calls that were refused, and the refused calls are usually what a security review and an incident retro both ask about first.
Related reading
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