Decision context
Record the proposed action, amount, merchant, principal, workflow step, and source evidence.
AI agent audit trail
Prove what the agent did and why it was allowed.
When an agent touches money, the hard question is not just whether it worked. It is who authorized the action, what evidence was available, which policy fired, and why the decision was safe.
Failure modes
Without a trace, every dispute becomes archaeology.
- Finance asks why a charge was approved and no one has the decision context.
- Support sees a refund but not the policy that allowed it.
- A team cannot tell whether the user approved a borderline action.
- The final chat answer omits the unsafe intermediate step.
Runtime check
What the trace should capture
Policy outcome
Attach the rule, cap, checker, and effect that shaped the response.
Human review
Preserve the reviewer outcome when a blocked or held action needs a person.
Audit proof
Useful audit records are action-level
ok Allow, cap, block, or hold effect
ok Reason shown to the app or operator
ok Trace id for follow-up
ok Review outcome for escalated decisions
Questions
What teams ask before they wire it in.
Why audit intermediate steps?
An agent can take an unsafe action even if the final response looks harmless. Action-level traces preserve the step that mattered.
Is metadata used for enforcement?
Metadata is useful for audit context. Enforcement should rely on the event fields and policy inputs your integration treats as authoritative.
Ship the check
Put a effect between the agent and the action.
Bring a real checkout, refund, booking, or invoice flow. We will map the exact pre-action check and the audit record it should leave behind.