AI agent governance
What an AI agent audit trail must record
Learn what an AI agent audit trail should record across prompts, model decisions, tool calls, approvals, external actions, retries, and evidence export.
Read guideAI audit evidence
Design AI agent audit trails that remain useful under review
These guides explain how to connect agent identity, authority, model activity, tool calls, approvals, retries, and external effects in a versioned audit log schema, then retain and export the evidence without turning sensitive data into an uncontrolled archive.
Updated August 23, 2026
Audit log schema
Design a versioned audit log schema for AI agents
Design a versioned AI agent audit log schema for identity, model runs, tool calls, approvals, outcomes, integrity checks, privacy, and review.
Read guideTool-call evidence
Turn AI tool-call logs into reviewable evidence
Design AI tool-call logging that records authorization, normalized inputs, results, errors, approvals, external effects, and tamper-evident exports.
Read guideEvidence lifecycle
Plan retention and export for AI audit evidence
Plan retention, legal holds, deletion, export manifests, checksums, access controls, and independent verification for AI agent audit evidence.
Read guideDefine the evidence boundary around a real AI workflow
Share the agents, models, tools, approval paths, data restrictions, and review obligations. Rantropy will identify the events and integrity controls that need to be captured and tested.