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?
// roles
Who uses this evidence
AI Code Review Context matters to different teams for different reasons. AgentPM keeps the same session evidence useful for the people building, reviewing, funding, and governing coding-agent work.
Team
Engineering Leaders
See how agent-assisted work is unfolding across a team before the only artifact is a polished pull request.
Read role pageRisk
Compliance and Security Teams
Review agent-assisted engineering work with the surrounding session context security teams need for risk triage and policy design.
Read role pageGovernance
Legal and Compliance Teams
Give governance teams a practical evidence trail for agent-assisted software work without pretending local coding sessions are model-call logs.
Read role pageDelivery
Product and Program Leaders
Understand how agent-assisted work moves from intent to implementation when the important context never made it into the ticket.
Read role page// connected use cases
Where this fits in AgentPM
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.
// comparisons
How to compare this layer
AgentPM is not trying to be the model trace, eval, gateway, ticket, or code host. It preserves the local coding-agent work that explains what happened before those systems have a clean artifact.
Browse comparisonsAI product evals and observability
AgentPM vs Braintrust
Braintrust helps teams evaluate, observe, and iterate on AI products. AgentPM captures how coding agents perform software development work.
Open-source LLM engineering platform
AgentPM vs Langfuse
Langfuse helps teams trace, evaluate, analyze, and manage prompts for LLM applications. AgentPM tracks the coding-agent work that creates software.
AI application observability
AgentPM vs Traceloop
AgentPM and Traceloop operate at different layers. Traceloop watches AI applications run; AgentPM watches software get built by coding agents.
// 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 inside AI systems. AgentPM tracks agent-assisted software work: what was asked, what the agent tried, what changed, what failed, what shipped, and what should become reusable knowledge.
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.