AI Agent Tracing Guide
What is tracing
Tracing records each operation in an agent run, including prompt generation, context retrieval, tool calls, and final outputs.
It preserves execution order and dependency relationships between steps.
Why tracing matters
Without tracing, teams only see symptoms. With tracing, they can identify exactly where behavior diverged from expectations.
This shortens debugging cycles and improves confidence in production changes.
Example trace breakdown
A trace can show the planner agent selecting the wrong tool, the retrieval step returning low-quality context, and the response agent compounding the error.
Because every step is connected, root cause becomes measurable instead of speculative.
Visualization benefits
Visual timelines help engineers and product teams align on what happened, why it happened, and what to change next.
This shared debugging surface improves incident response across the entire AI stack.