Replayable audit record
Also called: agent run replay, reasoning trace audit
An audit record complete enough to reconstruct an agent run step by step, including the reasoning behind each action.
What replayable audit record means.
A replayable audit record captures enough of a run to reconstruct it rather than summarize it: the inputs, each step in order, the model that handled it, the reasoning trace that produced the proposed action, the policy decision that permitted it, any human approval, and the resulting effect. The test is whether a reviewer who was not present can determine why a specific action was taken. Conventional application logs record that something happened; a replayable record preserves the decision path that led there.
Why it matters.
When an agent makes a consequential mistake, the questions that follow are all about cause: what did it see, what did it conclude, what was it allowed to do, who approved. A record that lists actions without reasoning cannot answer any of them, which leaves an organization unable to distinguish a policy gap from a model error, and unable to demonstrate diligence.
How it works in practice.
Reasoning is captured with the action
The trace that led to a proposal is stored alongside it, so the record shows intent and not only effect.
Ordered and complete
Steps are recorded in sequence with their inputs, so the run can be walked rather than inferred.
Policy and approval decisions included
Each authorization outcome and each human approval is part of the record, with the identity of the approver.
Tamper-evident storage
A release may use cryptographic linkage and append-only storage, but exact coverage, verification, and replay integrity require release-specific evidence.
What people get wrong.
That verbose logging is the same as replayability. Volume is not the constraint; completeness of the decision path is. A log can be enormous and still omit the reasoning, the policy decision, and the approver, which are exactly the fields a reviewer needs.
Common questions.
What makes a record replayable rather than just detailed?
Whether it contains the decision path. Inputs, ordered steps, the reasoning behind each proposed action, the policy decision, and the approver. With those a reviewer can reconstruct why; without them they can only see what.
Does storing reasoning traces create a data-sensitivity problem?
It can, because traces may contain material as sensitive as the underlying data. Traces are therefore stored under the same classification and access controls as the data they derive from, not in a separate lower-controlled log.
How long should records be kept?
By the approved retention policy for the environment and applicable obligations, not by a universal platform default. Deletion, archival, integrity checking, legal holds, and verification after archival must be tested for the release.
Standards referenced.
Trunnion is engineered to align with these control families. Alignment by design is not a claim of current certification or authorization.
Read next.
- Hash-chained audit trailAn append-only log where each entry includes the cryptographic hash of the previous one, so any later alteration is detectable.
- Agent governanceThe control layer that decides what an AI agent may do, who approves consequential actions, and how every step is proven afterward.
- Human-in-the-loop approval gateA checkpoint where an autonomous agent pauses and a named person approves, edits, or rejects a consequential action before it executes.
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