Short answer
An AI agent transaction audit explains what an agent did on-chain, which policy allowed or blocked it, what risks were detected, and whether any permissions remain open. It turns transaction data into a record that humans can review.
When this matters
- A DAO wants to publish evidence that agent spending stayed inside approved limits.
- A protocol needs a post-incident timeline for agent signatures and contract interactions.
- A wallet provider wants customer-facing explanations for risky approval prompts.
- A compliance team needs exportable records for internal controls and vendor review.
Operating steps
- Collect wallet addresses, transaction hashes, agent policy, and reviewer notes.
- Classify each action as swap, bridge, approve, claim, mint, or transfer.
- Compare every action with the policy version active at that time.
- Attach risk labels for infinite approve, unknown contract, duplicate signature, or abnormal gas.
- Export a report with evidence, timeline, exceptions, and open revocations.
Common risks
- A raw transaction table rarely explains intent, policy, or governance context.
- Policy changes must be versioned or an audit can judge old actions by new rules.
- Approval status can change after the transaction and must be checked again.
- Evidence gaps make community review slower and less credible.
How Web3Agent Permit fits
Web3Agent Permit builds audit reports that connect transaction samples, active policy, risk flags, webhook events, and revocation status.