Agent Observability

The instrumentation and tooling that makes agent runs inspectable — traces, logs, metrics, replays — so failures can be debugged and improvements measured.

Agent observability is to agent systems what APM is to web apps — except agents are non-deterministic, long-running, and decompose into branching trees of tool calls. Without observability, debugging agents is impossible. With it, you can replay a problematic run, find which step diverged, and fix the prompt, tool, or harness.

The minimum viable observability stack: structured logs per tool call (input, output, latency, cost), full trace per session (the decision tree), and aggregate metrics (success rate, mean iterations, cost per task). Tools like LangSmith, Helicone, Langfuse, and the OpenAI/Anthropic dashboards provide this out of the box. Skipping observability is the single most expensive shortcut a team can take in production agent deployment.