← All answersSources checked: 2026-06-23
AI agent audit trail for process intelligence
What to log when AI agents suggest or execute process work: model state, evidence, policy, approval, action, result, and review.
Direct answer
An AI-agent audit trail should connect the process step, model state, input evidence, suggested action, policy or role boundary, human approval, execution result, and post-action review. That record is what turns an agent action into governed process work.
Best fit
- Geist fits teams that want AI suggestions tied to BPMN, event evidence, ownership, approval, execution, and review history.
- A standalone chatbot may be enough for advice, but process work needs traceability once actions affect operations.
Pressure points
- The log should show why the agent suggested the action and what happened after execution.
- Approvals should bind to the exact action and process context.
- Review should capture whether the action improved the process.
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FAQ
Frequently asked questions
01What should an AI-agent audit trail include?
Process step, model version or state, data source, prompt or instruction summary, suggested action, approval, execution result, exception handling, and review outcome.
02Why is process context important for the audit trail?
Because the same action can have different risk depending on process state, owner, customer, jurisdiction, control, and downstream effect.
03How does Geist position this?
Geist positions agents inside the process loop: they read the model, use evidence, propose actions, route approvals, and leave history for review.
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