---
title: "What is an audit trail in AI agents?"
description: "An audit trail records what an AI agent knew, from which sources, at what time, so past actions can be explained. Definition and what to log."
canonical: https://past.dev/glossary/audit-trail
last-updated: 2026-09-02
---
# What is an audit trail in AI agents?

Source: https://past.dev/glossary/audit-trail

An audit trail is the record that lets a past action be explained: what was known, from which sources, at what time. For AI agents the trail must cover memory as well as actions: which facts were recalled, from which sources, and what the store contained at that moment. Regulators, customers, and the team's own debugging all consult the same record.

## What an audit trail records

- **Input**: what the agent was asked, and when.
- **Knowledge**: which memory was recalled, with per-fact sources and dates ([provenance](/glossary/data-provenance)).
- **Action**: what the agent answered or did.
- **State**: what the store contained at that time, which requires [ingestion time](/glossary/ingestion-time) so knowledge can be reconstructed as of the action.

> **Scope**
>
> Log recall as well as action. An agent's behavior is explained by what it remembered at the moment it acted, and that set changes as ingestion continues.

## Why it matters for AI agents

Agents act on accumulated memory rather than only on the visible prompt, so the prompt log alone no longer explains behavior. When an agent sends a wrong figure, the question is which stored fact supplied it and which source produced that fact. Two records answer it together: the recall log for that action, and the per-fact provenance of what recall returned.

Record-keeping duties also attach to automated decisions in several regulatory regimes; [EU AI Act record-keeping](/guides/eu-ai-act-record-keeping) surveys one of them. past.dev supplies the memory half of the trail: recall returns evidence with dates and sources, giving each answer a citable basis.

## An audit trail in practice

On July 7, 2026 an agent tells a customer their contract renews on September 1. In October the customer disputes it. The trail resolves the dispute line by line.

1. July 7, 10:14: the renewal query returned two facts.
2. Current fact: renewal date September 1, 2026, from the signed order form dated February 12, 2026.
3. Superseded fact: renewal date July 1, shown as closed since the February amendment.
4. Store state: the order form had been ingested March 3, 2026, well before the answer.

Each line is dated and attributable. The [AI audit trail guide](/guides/ai-audit-trail) covers what to log and for how long; [data retention](/glossary/data-retention) governs how long the trail itself is kept.

## Related concepts

- **[Data provenance](/glossary/data-provenance)**: per-fact origin; the trail is built from it.
- **[Evidence status](/glossary/evidence-status)**: the recall-time label recorded in the trail.
- **[Ingestion time](/glossary/ingestion-time)**: the axis that reconstructs what was known when.
- **[Data retention](/glossary/data-retention)**: how long trail records are kept.
- **[Grounding](/glossary/grounding)**: answers tied to sources at response time.

## Frequently asked questions

### What should an AI agent audit trail contain?

The request, the memory recalled with its sources and dates, the action taken, and enough state to reconstruct what the system knew at that moment. Together these let a past action be explained without guesswork.

### Why is an audit trail harder for agents with memory?

Behavior depends on facts accumulated over months, so the prompt alone does not explain an action. The trail has to reach into memory: which stored facts were used and where each came from.

### How long should audit records be kept?

Long enough to cover disputes, audits, and any record-keeping obligations that apply to the deployment, which vary by industry and region. Retention for audit records is usually set separately from retention for the underlying data.

## Related

- [Data provenance](https://past.dev/glossary/data-provenance)
- [Evidence status](https://past.dev/glossary/evidence-status)
- [AI audit trail guide](https://past.dev/guides/ai-audit-trail)
- [Memory API quickstart](https://past.dev/docs/memory-api/quickstart)
- [Benchmark methodology](https://past.dev/benchmarks/methodology)