Review AI-Generated Code in Cursor

Cursor, Anysphere’s AI code editor, is a place where coding agents can read a repo, edit files, and help prepare changes for review. The practical question is how an engineering team keeps those changes reviewable when more code is drafted by agents. The best answer is to make AI-generated code pass the same review gates as human code, then add two agent-specific checks: provenance and scope. This is a good first workflow for ai coding training for teams because it changes daily habits, not just prompts.

Put the review boundary in the repo

AI coding governance is the set of repo rules, permissions, review habits, and audit trails that keep coding agents useful without making their output unreviewable.

Start by writing the review boundary where the agent will actually see it. In Cursor, that usually means a small rule file for the IDE workflow plus an AGENTS.md note for repo-level constraints. Keep it boring: what the agent may edit, what it must not touch, what tests prove the change, and what the reviewer needs to see.

A real boundary might say: “The agent may edit files under packages/web/src/**, but database migrations need a human-written plan first.” That is much better than “be careful with the database,” because it gives both Cursor Agent and the reviewer the same line to enforce.

The trap is hiding the policy in a kickoff prompt or a Slack thread. Prompts disappear. Repo rules travel with the code, show up in review, and give engineering team ai adoption a stable place to improve.

For a broader training path around this pattern, see the related training topic.

Compare the three review surfaces

You need more than one review surface because agents make different mistakes at different times. A Cursor rule can prevent an obviously wrong edit before it happens. The IDE diff catches local surprises while the context is fresh. The pull request catches integration risk, ownership, and deployment impact.

Verdict: repo rules win for prevention, the IDE diff wins for fast author review, and the pull request wins for shared accountability. Use all three for production code. If the change is tiny, the PR can be light, but the agent should still leave a clear trail.

Review the diff, not the whole chat

The reviewer should not have to replay a 200-turn agent conversation. Ask for a handoff receipt instead: what changed, why it changed, what files are risky, what tests ran, and what the agent was not allowed to do.

In Cursor, this works well when the author uses Agent to draft the change, reviews the local diff in the editor, then pastes a compact receipt into the PR. The reviewer can then inspect code and test evidence like any other change.

The agent-specific check is provenance. If a new helper function appears, the author should know whether it came from existing repo patterns, public docs, or a model guess. If your team often loses useful context from agent sessions, CodeAlmanac Saves What Agent Chats Forget is a related pattern for turning chat history into reviewable memory.

The trap is treating the chat transcript as proof. It is not. The proof is the diff, the tests, the linked issue, and a human author willing to own the change.

Keep MCP permissions small at first

MCP, the Model Context Protocol, is a standard way for AI tools to connect to external systems such as repos, issue trackers, docs, databases, and internal services.

That makes MCP useful for agentic coding, but it also changes the review problem. If a coding agent can read GitHub issues, inspect docs, and query a database, reviewers need to know which external context influenced the patch.

Start with read-only MCP access for code review workflows. For example, allow the agent to read GitHub issues and internal docs, but do not let it write Jira tickets, mutate production data, or approve pull requests. Add write actions only after the team can answer: who authorized it, where is the log, and how would we undo it?

The trap is giving the agent broad tool access because the first demo feels smooth. Smooth is not the same as safe. Small permissions make the review story much easier to explain.

Train the team on one repeated loop

The fastest way to implement code review habits for ai-generated code is to make one small loop mandatory for agent-authored changes. Scope the task, generate the change, review the IDE diff, run tests, write the receipt, then open the PR.

That loop is simple enough for an ai coding workshop, but it still respects real engineering pressure. It does not ask reviewers to become prompt auditors. It asks them to keep doing code review, with a few new checks for scope, provenance, and tool use.

Run a short weekly sample for the first month. Pick five agent-assisted PRs and ask: Was the scope clear? Did the tests match the risk? Did the author catch anything before review? Did the agent touch files outside the boundary?

The trap is measuring only “time saved.” Developer productivity improves when review quality, rollback confidence, and maintainability stay healthy. A fast unsafe merge is just borrowed work.

Paste this Cursor review checklist

Use this as a starter Cursor rule. Keep it short enough that people will actually maintain it.

# .cursor/rules/ai-review.mdc
---
description: Apply when reviewing code drafted or edited by Cursor Agent or another coding agent.
globs: ["**/*"]
alwaysApply: false
---

## AI-generated code review checklist

Before opening a PR, the author must leave a short handoff receipt:

- Scope: What task was the agent asked to complete?
- Boundary: Which files or systems were intentionally out of scope?
- Diff review: Which generated edits did the human author reject or rewrite?
- Tests: What commands ran, and what was not tested?
- Provenance: Did any logic come from external docs, MCP tools, snippets, or model guesses?
- Risk: What could break in production if this change is wrong?
- Ownership: Who is the human owner of this PR?

Reviewer checks:

- The change is small enough to review without replaying the chat.
- The diff matches the stated scope.
- New behavior has tests or a clear reason tests were not added.
- Security, auth, billing, data deletion, and migrations received explicit human review.
- MCP or external-tool context is named when it influenced the code.
- The PR can be reverted without hidden follow-up work.

Suggested PR receipt:

Agent-assisted: yes
Human owner:
Scope:
Tests run:
High-risk files:
External context used:
Reviewer focus:

Common questions

• What are the best ways to implement code review habits for ai-generated code?

  The best way is to keep normal code review and add three agent-specific checks: scope, provenance, and tool permissions. Use one repo rule, one PR receipt, and one review checklist. That gives the team a repeatable habit without asking every reviewer to inspect the full agent conversation.

• Should reviewers read the agent chat before approving?

  Usually no; reviewers should approve the diff, tests, and receipt, not the entire chat transcript. The caveat is high-risk work. For auth, billing, migrations, or data deletion, the reviewer may ask for the relevant prompt, tool output, or design note that shaped the change.

• Where should Cursor rules and AGENTS.md each live?

  Put Cursor-specific review behavior in .cursor/rules/*.mdc, and put portable repo constraints in AGENTS.md when your team uses that convention across tools. A good split is simple: editor workflow in Cursor rules, architecture boundaries in AGENTS.md. Nested files help when one monorepo contains different services.

• How does this help engineering team ai adoption?

  It makes adoption reviewable instead of vibes-based. Teams can compare agent-assisted PRs against the same checklist each week: scope clarity, test evidence, rejected edits, and risky files. That turns engineering team ai adoption into an operating model, not a one-off training session.

• Do we need different rules for Claude Code and Cursor?

  Yes, keep product-specific workflow notes separate, but share the repo constraints. Cursor rules should describe Cursor Agent behavior and review expectations. Claude Code, Anthropic’s coding agent, may use different local commands or memory conventions, while the same PR receipt can still travel across both workflows.

Best ways to use this research

• Best for: Engineering teams already using Cursor Agent on production repos and needing lightweight code review guardrails before expanding agentic coding.
• Best first artifact: Add the .cursor/rules/ai-review.mdc checklist above to one active repo, then require the PR receipt only for agent-assisted changes.
• Best comparison angle: Compare prevention, author review, and PR review separately. A rule is not a substitute for a reviewer, and a reviewer is not a substitute for a clear agent boundary.
• Best workshop exercise: Have developers run the same small bug fix with Cursor Agent, reject at least one generated edit, and write the receipt before opening the PR.

Further reading

• Cursor — Agent
• Cursor — Rules
• Model Context Protocol — specification
• Claude Code — getting started

Start with one repo

Pick one repo, add the checklist, and use it on the next five agent-assisted pull requests. After that, keep the parts reviewers actually used and delete the rest.

One methodology lens

One useful way to read this through our methodology is the Plan step: delegate first-pass decomposition and dependency mapping, review the sequencing and assumptions, and keep ownership of scope and priorities. If that split is still fuzzy, the workflow usually is too.