AI Code Review Workflow for Teams

AI can help write code, but a human still owns the change, the evidence, and the merge. An AI code review workflow is a small team convention for how engineers disclose, test, review, and merge code that a coding agent drafted, changed, or checked. If your team is split on whether to use AI assistants, you do not need to settle that fight first. Agree on what a pull request must show, and the tool wars mostly go quiet.

Start with the next PR, not a grand policy. A policy nobody reads changes nothing. A one-line disclosure on the next change ships today.

See why the split is really about review

The real problem is rarely that one engineer uses Cursor and another refuses. It is that the team cannot tell which parts of a diff were generated, which assumptions got checked, and which outside systems the agent touched. That is a review gap, and a review gap follows you across every assistant.

Picking one approved tool and writing a long policy feels decisive, but it does not fix the diff that lands in your queue. Reviewers still get large changes with thin context, tests that do not map to the change, and agent output that sounds more sure of itself than it should.

So standardize the review contract, not the personality of the assistant. The surfaces differ, the review burden does not. Cursor supports project rules and agent workflows. Claude Code supports CLAUDE.md, skills, hooks, MCP, and slash commands. Codex supports AGENTS.md, MCP, and verification loops. A good convention says what gets disclosed, what gets verified, what tools are allowed, and what evidence a reviewer can ask for. You can teach all of that in AI coding governance and run it this sprint.

Fix the four ways AI reviews go wrong

Most failures fall into four buckets, and each has a cheap fix.

Invisible authorship comes first. A PR shows up with no note that an agent drafted the migration or suggested a security-sensitive regex. The fix is not shame, it is a short disclosure line: assisted areas, human-edited areas, and files you want read closely.

Over-trusting local tests is second. An agent can write code that passes the narrow test it just wrote while missing the integration behavior. The fix is evidence mapping. Each non-trivial change names how it was checked: unit test, integration test, manual check, static analysis, or a reasoned no-test exception.

Tool sprawl is third. MCP, the Model Context Protocol, lets AI applications connect to outside tools and data through defined servers. That is useful, and it means you need boundaries around GitHub, issue trackers, databases, design files, and logs. The fix is an allowlist by task type, not a blanket yes.

One flat instruction file is fourth. Teams dump every rule into a single root memory file and hope the agent reads the room. The fix is scoped guidance: repo-wide rules at the root, service rules near the service, review rules where contributors already look.

Here is how the durable artifact maps across the three main tools:

A reviewer should never need to reverse-engineer the agent session from the final diff.

Copy this team review convention

Keep it short. If it needs a meeting to explain, it is too long for a busy review queue. Paste this into your repo and trim what you do not need.

# AI-Assisted Code Review Checklist

Use this for any PR where a coding agent (Cursor, Claude Code, Codex,
or another) drafted, edited, reviewed, or tested code.

## Author disclosure
- [ ] I named the areas where AI assistance was used.
- [ ] I named the areas I rewrote or verified manually.
- [ ] I marked any files that need closer human review.

## Change control
- [ ] The PR is small enough to review as one change.
- [ ] Generated code I do not understand was removed or rewritten.
- [ ] Public API, auth, data model, billing, security, or infra
      changes are called out explicitly.

## Verification evidence
- [ ] Unit, integration, static analysis, or manual checks are listed.
- [ ] New tests map to the behavior changed, not just the agent's path.

## Recovery
- [ ] The fastest safe undo path is noted next to risky changes.

When you connect this to the way your team plans work, the Plan step does most of the heavy lifting: let the agent do first-pass decomposition and dependency mapping, then review the sequencing and keep ownership of scope and priorities. If that split feels fuzzy, the workflow usually is too.

Know when the convention is working

You will know it is working when a reviewer can approve or reject from the artifact and the evidence alone, without replaying a long session to figure out what changed. That is the whole point: a short, visible place where scope, allowed tools, expected tests, and the rollback path live before generated code reaches review.

Watch three signals. Pull requests name the rule they followed. They include the checks they promised. And nobody has to reconstruct the agent session to understand the change. If those hold, you have a working convention instead of a hopeful one.

Common questions

How should a team start with an AI code review workflow?

Start with one visible team rule, not a loose preference. Add a short repository convention, a review checklist, and one owner who can reject agent output when the evidence is missing. Do it on the next pull request rather than waiting for a full policy, since the smallest real change teaches faster than the longest document nobody opens.

Where to go next

Treat the agent as a fast implementer behind a receipt gate: it moves quickly only when scope, checks, and ownership stay visible. Start from AI coding governance.

Further reading

• MCP specification
• Cursor Agent docs
• Claude Code docs
• Codex CLI docs
• genai.owasp
• NIST AI Risk Management Framework
• Codex on GitHub
• Anthropic skills on GitHub