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.
// who it helps
Engineering teams reviewing code created or modified with coding agents.
// the painful moment
Why this matters
A PR shows what changed, but not how the agent got there. Reviewers often need the session context to understand intent, verification, false starts, and unresolved risks.
What AgentPM captures
- Prompt intent and agent implementation path.
- Commands, tests, errors, and verification attempts.
- Decisions, assumptions, and open loops extracted from the session.
What teams can do
- Review risky changes with better context.
- See which checks the agent ran before opening a PR.
- Separate polished output from unverified assumptions.
Questions this answers
- Why did the agent choose this approach?
- What tests failed before the final code?
- What should the human reviewer double-check?
// fit
What AgentPM is not replacing
AgentPM does not replace code review. It gives reviewers the session evidence that makes agent-assisted review less opaque.
A typical workflow
- 01Capture the implementation session.
- 02Open the notebook or insights for the relevant work.
- 03Review commands, errors, artifacts, and decisions.
- 04Use the evidence to focus human review on the right risks.
// common questions
Questions about ai code review context
Can AgentPM help review agent-generated code?
Yes. AgentPM helps reviewers inspect the session context behind agent-assisted code, including what the agent attempted, verified, changed, and left unresolved.
Does AgentPM automate code review?
No. AgentPM supports human review by preserving evidence. It is not a replacement for reviewer judgment, tests, or secure development practices.
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
Evidence
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.
Codex
Codex Session Tracking
Track Codex work from prompt to local commands, file edits, test runs, open loops, and review-ready evidence.
Cursor
Cursor Session Tracking
Capture Cursor agent transcripts alongside Claude Code, Codex, and Grok work so teams can review prompts, edits, tool output, and unresolved loops in one evidence layer.