AI Tracing
AI tracing records the linked steps in one AI run, such as model calls, search, tool use, and agent work. The record helps people follow the run from start to finish.
Another example
A failed answer has trace ID 8f2. Its record links search scores, model delay, tool inputs, and the blocked result. Saving raw prompts without consent or hiding private details is a privacy problem, not good monitoring.
Main parts
- 01trace identifier
- 02correlated spans
- 03redacted attributes
Use it when
Use this term when finding a problem or measuring one AI run across model and non-model steps.
Do not use it when
Do not collect private content by default, and do not call a summary of many runs one trace.
Name used in code
trace → spans → events + attributes Before you ship
Check the model and prompt versions, source data, versioned evaluation set, and measures. Verify links to evidence, tool permissions, privacy, common failures, decline-to-answer and fallback behavior, human review, monitoring, cost, speed, and rollback.
Outcome: Use or evaluate AI Tracing to make AI behavior measurable and tied to evidence. User context: A failed answer has trace ID 8f2. Its record links search scores, model delay, tool inputs, and the blocked result. Saving raw prompts without consent or hiding private details is a privacy problem, not good monitoring. AI method or concept: AI Tracing. Why it fits: Use this term when finding a problem or measuring one AI run across model and non-model steps. Do not use it when: Do not collect private content by default, and do not call a summary of many runs one trace. AI requirements: Define the input and source evidence. Set model and tool permissions. Use versioned evaluation data and measures. Define failure, decline-to-answer, privacy, speed, and cost limits. Operational safeguards: Keep a clear trace and hide sensitive log data. Show users a safe fallback. Mark steps that need human review. Define how to roll back the model or prompt. Acceptance criteria: Record baseline and target measures on a versioned evaluation set. Test edge cases and hostile inputs. Verify fallback, monitoring, permissions, and rollback. Evidence and limits (evidence boundary): It is official for that source. Other platforms or teams may use the term in another way. Unknowns to confirm: Target task, model and version, evaluation owner, source data, risk limit, tool permissions, and production fallback.
Check this request
Review the current use of AI Tracing. Definition: AI tracing records the linked steps in one AI run, such as model calls, search, tool use, and agent work. The record helps people follow the run from start to finish. Release checks: Check the model and prompt versions, source data, versioned evaluation set, and measures. Verify links to evidence, tool permissions, privacy, common failures, decline-to-answer and fallback behavior, human review, monitoring, cost, speed, and rollback. Before changing code, report the evidence you found, gaps, severity, and the smallest safe fix.