Audit Trail
A complete, immutable record of what the system did, when, why, and with what inputs.
An Audit Trail is the operational backbone of accountability. It records every action the agent took, every decision it made, every tool it called, and every input it received — in a format that can be reviewed, searched, and analyzed after the fact. Audit Trails serve multiple purposes: debugging (what went wrong?), compliance (can we prove what happened?), optimization (where are we wasting tokens?), and learning (what patterns lead to good outcomes?). An immutable Audit Trail also protects against Execution Hallucination — the agent can claim it did something, but the trail shows what actually happened.
More in System Transparency
System Opacity
The inability to see what an AI system knows, why it made a decision, or what it did. The default state of most AI tools today.
Decision Fog
The user can see the output but not the reasoning, trade-offs, or alternatives the model considered.
Black Box Agency
An agent that acts on your behalf but won't show its work. You get results with no audit trail.
Invisible State
The system has internal state that affects behavior but is not exposed to the user.
Accountability Gap
No one — human or system — can explain why a specific output was produced.
Reasoning Visibility
The user can inspect the model's chain of thought, assumptions, and decision points.
Decision Traceability
Every output can be traced back through the reasoning, context, and data that produced it.
Open State
The system's internal state is visible and inspectable at any time. The opposite of Invisible State.