Glossary

What is AI agent observability?

AI agent observability is the practice of instrumenting AI agents so you can see what they did and why: capturing traces, spans, tool calls, token usage, latencies, and errors across a run. It answers what happened after the fact, turning an opaque agent loop into a replayable record you can debug and measure.

Observability is where teams start when agents reach production, because you cannot improve (or trust) a loop you cannot see. Traces reconstruct the sequence of reasoning and tool calls; metrics track cost, latency, and error rates; evaluations score output quality over time.

Observability stops at watching. It has no opinion on what an agent is allowed to do, no gate that holds a risky change before it lands, and no path for a human to intervene mid-run. It supplies the evidence for those decisions without making any of them, which is the line between an audit trail you answer for and a dashboard you glance at.

How it relates to agent management

Observability supplies the evidence that AI agent management acts on. Management adds what observability lacks: permissions, review gates, and intervention on top of the record.