Direct answer
An audit trail records each run as actor, reason, evidence, artifact, model version and cost, written append-only so it cannot be quietly revised. Refusals are recorded alongside successes, because a control that has never refused anything has not been demonstrated. The test is whether a past decision can be reconstructed without the engineer who built it.
AI agent audit trail
An append-only record of what an agentic system proposed, who decided it, on what evidence, what was executed as a result, and under which model version. It exists so a specific past action can be explained later without relying on anyone's memory.
What each run has to carry
Storing that a call happened is not an audit trail. These are the fields that make a decision reconstructable.
Actor
Which agent or person initiated the run, and whose authority the call carried.
Reason
Why the action was proposed, and the reason a human gave when approving or rejecting it.
Evidence
The specific sources relied on, so a claim can be traced rather than trusted.
Artifact
What was actually produced or changed, read from the system rather than reported by the agent.
Version
The model, prompt and tool-contract versions in force, so behaviour can be tied to a release.
Cost
Tokens, calls and duration, which is how a quiet climb in retries becomes visible.
Append-only, and why it matters
A record that can be edited answers a weaker question than one that cannot. Enforcing append-only in the database rather than in application code means a mistaken row stays visible and is corrected by a later entry, which is what makes the trail evidence rather than a report.
Reading it back
The trail earns its keep when someone asks why a specific action was taken. Design for that query from the start: given one artifact, return the approval, the reason, the evidence and the version in one read. Backfilling this after an incident is considerably harder than writing it at the outset.
The Propose–Decide–Execute pattern
CodeDTX builds agentic systems on the Propose–Decide–Execute pattern: agents may only write proposals with evidence attached, a named human records an approval or rejection with a reason, and a separate execution layer carries out approved work and logs the artifact. No agent holds a write tool to the outside world.
- 01
Propose
The agent analyses live system state and drafts a change, with the evidence it relied on attached to the proposal.
- 02
Decide
A named human approves, edits, or rejects with a reason. Risk tier determines who is allowed to decide.
- 03
Execute
A separate execution layer performs approved work — merge, publish, call, write — and records the resulting artifact.
- 04
Audit
Actor, reason, evidence, artifact, tokens, and cost are retained for every run, so any decision can be reconstructed later.
The six layers we build and review against
- 1
Agent layer
Agent architecture, tool use, memory and context, multi-agent patterns, structured outputs, orchestration.
- 2
Integration layer
MCP servers, tool contracts, API and database adapters, authentication, permissions, legacy system access.
- 3
Knowledge layer
Retrieval and RAG, vector and search architecture, enterprise knowledge sources, data access controls.
- 4
Reliability layer
Evals, tracing, observability, cost and latency budgets, fallbacks, regression tests.
- 5
Safety layer
Guardrails, prompt-injection defense, PII and data boundaries, human-in-the-loop gates, audit trails.
- 6
Product layer
The application people actually use: interfaces, approval queues, and operational runbooks.
Frequently asked
Is application logging enough?
Usually not. Logs record that calls happened; an audit trail records why an action was authorised and on what evidence. Logs also rotate and are rarely append-only, so they cannot answer a question about a decision made months ago.
Should refused actions be recorded?
Yes, and they are often the more useful half. A refusal demonstrates the control worked, and a rising refusal rate is the earliest signal that inputs or interfaces have changed underneath the agent.
How long should records be kept?
As long as the decisions they describe remain consequential, which is usually governed by the same policy as the underlying business records rather than by anything specific to AI.
Have a workflow that should become AI-enabled?
Tell us about the system it lives in. We reply from an engineering seat, not a sales deck.