AI Decision Audit Trail
A local-first audit record that turns AI decision-making into something you can reconstruct and review after the fact.
Runs on an internal decision runtime.
The problem
/ 01Autonomous and AI-assisted decisions are increasingly consequential, but their reasoning is usually invisible after the fact. Regulators and internal governance need a way to reconstruct what was decided, why, and on what inputs.
What it does
/ 03Logs each AI decision with its inputs, context, and reasoning for later review.
Provides a reviewable, immutable trail for compliance and governance.
Technical note: runs on an internal decision runtime — local-first, dependency-light, under your rules.
Who it’s for
/ 03- Governance and compliance teams reviewing AI-assisted decisions
- Operators of autonomous agents that need an auditable record
- Teams that want local-first, dependency-light logging without a third-party service
In context
/ 01The trail plugs into the Agent Action Governor — policy decisions and the approvals that follow them land in the same auditable record.
Spec & limits
/ limits| Interface | Input limits | Output artifact | Failure mode | Non-claim |
|---|---|---|---|---|
Studio ephemeral /demo; pilot durable /v1 on your infrastructure. | TBD — verified in pilot acceptance | TBD — verified in pilot acceptance | TBD — verified in pilot acceptance | TBD — verified in pilot acceptance |
Related guide
/ guideSee the AI decision audit trail guide for a deeper problem walkthrough and verification notes — evaluation notes for the live demo and for a private pilot. Also: try the live experiment and read the product overview.
Frequently asked questions
/ 0501 What exactly gets recorded for each AI decision?
The request, the policy evaluated, the action taken, and the evidence produced — hash-bound and receipted so reviewers can replay the decision after the fact.
02 Can I audit decisions after the agent has finished running?
Yes. Each decision emits a signed, hash-chained receipt you can verify offline — including on fully disconnected infrastructure.
03 How does this integrate with an allow / deny / escalate policy gate?
Every decision is bound to the policy version that authorized it; disallowed actions are blocked pre-action and recorded as denials, so the trail proves what was allowed.
04 Is the audit trail queryable across multiple agents or sessions?
Yes — receipts share a hash-chained index you query without centralizing raw logs; each entry verifies independently.
05 Do I need a hosted platform to store the decision records?
No. Components run on your infrastructure with no required cloud dependency for receipt generation or verification. Studio demos run on an isolated host for pre-production evaluation.