AI agent observability collects structured traces, logs, metrics, status, tool calls, permission decisions, errors, and result evidence. It helps operators answer what the agent is doing, why work stopped, which resources were used, and whether the outcome satisfied the task.
Observability should retain actionable evidence while minimizing sensitive data. Missing telemetry must be reported as degraded visibility rather than interpreted as inactivity, and access to logs should follow the same privacy and authorization boundaries as the underlying work.
ELI5
Agent observability makes an AI agent's work visible enough to understand and operate. It records useful information such as the agent's current step, tool calls, errors, resource use, and evidence for the final result.
For example, if an agent stops while preparing a report, observability can show whether a source was unavailable, a permission was denied, or the agent encountered invalid data. Without that evidence, an operator sees only that the task did not finish.
What should AI agent observability show?
It should show identity, runtime state, task progress, tool activity, permissions, errors, resource use, and verification evidence.
Why are AI agent logs sensitive?
Logs can contain prompts, files, tool inputs, identifiers, errors, and internal business context that require protected access.
