Agent Observability
Agent observability is the practice of making an autonomous agent's behavior inspectable: traces of every run, metrics on quality and cost, and guardrail telemetry — so teams can see what agents did, why, and where they went wrong. It records; it does not decide.
What agent observability means
Observability answers "what did the agent do and why?" — traces of each run showing prompts, model calls, tool invocations and outcomes; metrics on completion rates, latency, spend and escalation; and guardrail telemetry showing what policies caught. It is indispensable for debugging and for the monitoring half of governance. The boundary to keep sharp is with enforcement: observability records what happened, but it does not decide what may happen — an observed agent is still an ungoverned agent if nothing checks its actions before they execute. That distinction (Forrester's "oversight must live outside the agent's execution loop") is why observability platforms and control planes are complementary rather than competing: the control plane enforces, observability illuminates, and both feed the same evidence store. Operationally, agent observability extends classic AIOps: new failure modes (looping, tool misuse, context rot) need new signals, and incidents involving agents need records that can answer "which agent, acting under which grant, decided what."
Frequently asked questions
What is agent observability?
Traces, metrics and guardrail telemetry that make agent behavior inspectable: what each run did, what it cost, where it failed, and what policies caught. It is the visibility half of governance — enforcement is the other half.
Is observability enough to govern agents?
No. Observability tells you what an agent did after it did it. Governance decides what it may do before. A traced agent that acts under a shared credential with no approvals is well-observed and ungoverned — the audit fails on attribution and authority, not on visibility.