Agent Audit Trail
Keep a reviewable audit trail of agent-assisted software work: what was asked, what ran, what changed, what failed, and what remained unresolved.
// who it helps
Technical leaders, reviewers, and teams that need accountable agent-assisted development.
// the painful moment
Why this matters
Agent-assisted work often leaves only a diff and a memory. When a risky change appears, teams need to know what happened before the final artifact existed.
What AgentPM captures
- Prompts, replies, commands, tool output, and session-level context.
- Detected decisions, artifacts, risks, errors, and unfinished work.
- Evidence references that point back to exact transcript turns.
What teams can do
- Review how agent-assisted work unfolded.
- Understand what the agent did and did not verify.
- Create accountability without forcing manual status reports.
Questions this answers
- What did the human ask the agent to do?
- What commands or checks did the agent run?
- What open risks did the session leave behind?
// fit
What AgentPM is not replacing
AgentPM does not replace compliance systems or source control. It provides the session evidence that helps teams understand agent-assisted engineering work.
A typical workflow
- 01Capture local agent sessions as work happens.
- 02Review extracted keyframes and session notebooks.
- 03Link evidence to PR review, incident review, or team follow-up.
- 04Use recurring findings to improve rollout guidance.
// common questions
Questions about agent audit trail
What is an agent audit trail?
An agent audit trail is a durable record of agent-assisted work, including the request, agent reasoning visible in the session, commands, tool output, decisions, and unfinished work.
Does AgentPM prove code is correct?
No. AgentPM does not prove correctness. It helps reviewers see the evidence around the work so they can make better engineering judgments.
What is AgentPM?
AgentPM is an evidence layer for coding-agent work. It captures local agent sessions, makes them searchable, and helps teams understand what happened before work becomes a pull request, ticket, or review.
How is AgentPM different from LLM observability?
LLM observability usually tracks model calls, traces, cost, latency, and evals. AgentPM tracks engineering work: what agents attempted, where they got stuck, what changed, and what evidence explains the session.
Does AgentPM replace GitHub, Jira, or Linear?
No. AgentPM captures the work that happens before those systems have a clean artifact. It complements code hosts, issue trackers, and review tools with searchable session evidence.
// related
Related use cases
Review
AI Code Review Context
Give reviewers the missing context behind agent-assisted code: prompts, commands, failures, decisions, and what the agent left for humans to verify.
Leadership
Engineering Lead Agent Visibility
Help engineering leads see where agent-assisted work is happening, where teams are stuck, and which practices should become repeatable workflows.
Category
Coding Agent Observability
See the coding-agent work that happens before a pull request, ticket, or review: prompts, commands, retries, files, decisions, and unresolved loops.