Agent Observability
Agent observability is the ability to see what autonomous AI agents are doing in production — the actions they attempt, the decisions applied, the approvals they trigger, and the outcomes — so operators can monitor, debug, and govern agent behavior.
Definition
Agent observability is the ability to see what autonomous AI agents are doing in production — the actions they attempt, the decisions applied, the approvals they trigger, and the outcomes — so operators can monitor, debug, and govern agent behavior.
Observing behavior, not just tokens
Traditional LLM observability focuses on prompts, tokens, latency, and cost. Those metrics matter, but they do not tell an operator whether an agent tried to do something it should not have. Agent observability widens the lens to behavior: which tools an agent called, which actions policy allowed or blocked, where approvals were required, and how runs progressed across multi-step workflows. It answers operational questions — what is the fleet doing right now, and is any of it concerning?
Governance-grade visibility
Because Cordum sits on the action path, the same events that drive enforcement also drive observability. A governance timeline presents a narrative view of policy decisions, scans, approvals, replays, and overrides; approval analytics surface how the human-in-the-loop queue is performing; and the audit trail provides the durable record behind it. Observability and governance reinforce each other: you can see what happened because the control plane was in the path that decided it, which is stronger than reconstructing behavior from sidecar logs after the fact.
Frequently asked questions
How is agent observability different from LLM observability?
LLM observability tracks prompts, tokens, latency, and cost. Agent observability tracks behavior — which actions were attempted, which policy decisions applied, and what resulted — so you can monitor and govern what agents actually do, not just how the model performed.
Why does sitting on the action path improve observability?
When the control plane is in the path that decides each action, the same events power both enforcement and visibility. You observe behavior directly from the decision point rather than reconstructing it from after-the-fact logs.
Related reading
Govern your AI agents with Cordum
Cordum is the agent control plane: policy-before-dispatch enforcement, human approvals, and a tamper-evident audit trail for autonomous AI agents.