Audit trails for coding agent changes

Effective audit trails for AI coding agents should link a requested change to the requester and execution identity, repository and session context, tools and commands used, resulting commits, validation evidence, human approvals, and deployment records rather than relying on conversation transcripts alone. Using OpenTelemetry-style traces and stable identifiers can connect agent activity with source control and CI systems, while distinguishing service accounts from the people who initiated work improves accountability. High-value evidence includes blocked commands, file modifications, pull requests, exact commit SHAs, test results, review decisions, and deployment details, with branch protections helping verify that the approved revision was actually merged. Organizations should establish retention, access, redaction, and telemetry-granularity policies to avoid collecting unnecessary sensitive content, and should regularly test whether a complete change history can be reconstructed for both successful and failed agent runs.

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