Imagine an autonomous procurement agent authorises a two million pound payment. Six months later, the transaction is investigated. The prompt has changed. The model has changed. The APIs it called have changed. The system prompt in effect at the moment of the decision may no longer exist anywhere, in any form.

Traditional logs tell you what happened. They rarely prove why it happened, and the gap between those two claims is exactly what matters once an AI system is making decisions with real consequences attached.

The problem

Most AI accountability today is retrospective. When something needs explaining, we reconstruct events after the fact, from logs, from version history, from whatever happened to still be running. That reconstruction is expensive, frequently incomplete, and sometimes flatly impossible: the evidence a real investigation needs tends to disappear faster than the consequences that make the investigation necessary in the first place.

Our observation

ACTA starts from a different assumption: the important artifact was never the model. It is the decision. Models get retrained, fine-tuned and replaced on a timescale of weeks. The decision a system made, at a specific moment, using specific inputs and a specific policy, is a fixed historical fact the instant it happens, whether or not anyone thought to record it properly.

Our approach

Every execution produces a signed attestation describing:

• the identity involved
• the authority exercised
• the evidence used
• the policy evaluated
• the outcome produced
• the cryptographic signature proving the record has not been altered since it was written

That record becomes immutable the moment it is created. Accountability is generated during execution, not reconstructed afterwards from whatever logs happened to survive.

Why it matters

The future of AI governance is unlikely to turn on anyone’s ability to fully explain a trillion-parameter model’s internal reasoning. It will turn on something narrower and more tractable: proving what happened, who or what authorised it, under which policy, and whether the record of any of that has since been altered. That is a fundamentally different problem from model interpretability, and it deserves infrastructure built specifically for it, rather than logging bolted onto a system after the fact.

Status: Research note