When an API call is made by a human developer, the audit trail is straightforward: an identity, a timestamp, and a request payload. When an autonomous AI agent executes a sequence of actions, the context is far more complex. Traditional logging is insufficient for answering the critical questions.
Cryptographic Provenance
Every action taken by the system generates a signed receipt and replayable evidence.
The Evidence Pack
Whenever a proposal is generated and evaluated, the system compiles an Evidence Pack. This is a signed, tamper-sensitive record containing:
- The original intent that initiated the action.
- The specific context state available at that time.
- The exact structured spec generated by the model.
- The policy evaluation result.
- The human approval signature if required.
- The final execution result.
Replayability for Trust and Debugging
Because every input and state transition is captured, any execution can be replayed:
- For auditors: This gives reviewers a chain from human intent to machine execution. It is evidence, not a certification.
- For engineers: This allows developers to load a failed execution state locally, inspect what the model saw, and debug the specific failure point.
Execution should not be a black box. It should leave a receipt that people can inspect.