Agent Tracing
Definition
The practice of capturing a complete, structured record of an agent's execution — every model call, tool invocation, decision point, and intermediate result — as a trace that can be inspected, debugged, and analyzed. Agent tracing extends traditional application tracing (spans, events, durations) with AI-specific metadata: prompts, completions, token usage, tool call parameters, and decision reasoning. A trace lets you answer: why did the agent do X? where did it go wrong? how much did this task cost?
Builder Context
Tracing is the single most important observability investment for agent systems. Without traces, debugging agent behavior is guesswork. Instrument every: model call (prompt, response, tokens, latency), tool call (name, parameters, result, duration), and decision point (what the agent considered, what it chose, why). Use structured trace formats (OpenTelemetry spans or LangSmith traces) so you can filter, search, and aggregate. The trace is also your audit log — for regulated industries, you need to prove what the agent did and why.