How to trace what your AI agent actually did

Production AI agent failures may require investigation even when runs complete without errors, particularly when customers dispute claimed actions, releases alter behavior, or compliance rules may have been violated. Agent traces provide connected evidence of model calls, tool arguments and results, errors, timing, and metadata such as user, session, prompt, and model versions, enabling investigators to reconstruct what the application recorded and compare the agent’s final response with the outcome of its tools. However, traces cannot prove uninstrumented actions, reveal masked data, recover sampled or expired records, or confirm that external systems persisted a requested change, so consequential actions must be verified against systems of record. Braintrust supports searches by tool name, text, metadata, time, and span-level conditions to locate individual executions, inspect them through hierarchical, chronological, and timeline views, and identify retries, loops, latency, and recovered errors. After validating a failure pattern in one trace, teams can query production-wide records, distinguish calls from affected runs, group results by users or releases, account for incomplete data, and save findings for shared incident response.

Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.