Observability (AI Agent)
Live and historical visibility into agent execution — what ran, on what data, with what result.
What is Observability (AI Agent)?
AI agent observability goes beyond LLM call logs. It needs to capture orchestration paths, tool invocations, approvals, retries, and downstream effects. See Agent Observability and AI Agent Observability: Logs, Traces, Audit.
What is an example of Observability (AI Agent)?
An order-management agent times out. The trace shows that the model repeatedly selected a slow inventory tool after receiving stale results, exceeded its retry budget, and never reached the approval step.
How is Observability (AI Agent) different from related concepts?
LLM observability focuses on model and prompt behavior. Agent observability includes that layer plus tools, memory, workflow state, permissions, approvals, and real-world actions.
What should enterprises evaluate for Observability (AI Agent)?
- Trace every step under one execution ID with parent-child relationships for parallel and delegated work.
- Record state transitions and policy decisions as structured events instead of relying only on free-text logs.
- Create alerts around task failure, unsafe action attempts, loops, cost spikes, unusual tool access, and evaluation regressions.
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Putting Observability (AI Agent) to work?
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