Choose Code Review Agents by Receipts

The best code review agent is the one your team can constrain, inspect, and hold to a repeatable review receipt. For most engineering teams, model choice matters less than the workflow around ai code review: repository rules, tool boundaries, and human sign-off.

Multi-agent orchestration is the practice of giving different coding agents narrow roles, shared context, and explicit handoffs. In Cursor, Anysphere's AI code editor, that usually means pairing Agent with Cursor rules, an AGENTS.md, and a review checklist your team can actually enforce.

Start with one review lane, not five agents

Pick one valuable review lane first. A good starter lane is: changed files, tests touched, risky assumptions, and a short receipt in the pull request.

This matters because code review ai becomes noisy when every agent is allowed to comment on everything. A frontend reviewer, migration reviewer, and security reviewer can be useful later, but only after you know what a good review output looks like.

The trap is treating multi-agent orchestration like a group chat. In a payments repo, do not ask three agents to “review the PR” and merge the longest answer. Ask one agent to review database writes, one to review API compatibility, and require both to produce the same receipt shape.

If you are building an engineering team training path, anchor it in the governance basics first. Our broader AI coding governance topic covers the team habits behind this: prompts, rules, review guardrails, and accountability.

Put repository rules where agents will see them

Write the durable rules once, close to the code. Use a root AGENTS.md for repo-wide expectations, then nest more specific files inside areas like apps/web/, packages/api/, or infra/ when local rules differ.

Cursor rules can carry the same intent in a format the IDE can apply during editing and review. For example, a .cursor/rules/review.mdc rule might tell Agent to check authorization changes, identify missing tests, and never approve its own patch.

This matters because agentic coding fails quietly when the model has to infer team norms from surrounding code. Rules turn invisible judgment into reviewable text.

The trap is putting every preference into one giant memory file. Keep durable rules short. Put task-specific requests in the chat or review prompt, not in always-on project memory.

Give each agent a boundary and a receipt

As of June 2026, teams commonly mix Cursor Agent, Claude Code, Anthropic's coding agent, and Codex, OpenAI's coding agent, across local IDE work, terminal workflows, and remote review tasks. That is fine, as long as each agent has a job boundary.

A useful boundary sounds like this: “Review only files under packages/billing/. Do not edit code. Produce a receipt with risks, tests, and follow-ups.” This makes llm code review auditable instead of vibes-based.

This matters when engineers ask for the best llm for code review. The better question is: which model follows your repo rules, cites the changed files, and admits uncertainty in a way your reviewers can use?

The trap is letting the reviewing agent become the implementing agent without a handoff. If the agent writes the code, a separate pass should review it. If the same agent must do both, require it to label implementation notes separately from review findings.

For a cross-tool comparison of this safety pattern, see Compare AI Coding Agents Safely.

Keep MCP access boring on purpose

Use Model Context Protocol (MCP) for integrations that genuinely improve review quality: GitHub issues, design docs, test logs, package metadata, or internal docs. Give agents the minimum access needed for the review lane.

This matters because review agents are only as safe as their tool permissions. A reviewer that can read CI logs and linked tickets is useful. A reviewer that can write to production systems while summarizing a PR is not.

The trap is wiring every system into every agent because it feels powerful. In an ai coding workshop, a good exercise is to draw the tool boundary before writing prompts: read-only GitHub, read-only docs, no Slack posting, no database writes.

Train reviewers to check the agent, not obey it

Make the human reviewer responsible for the receipt, not for the agent's confidence. The reviewer should ask: did it inspect the right files, did it cite the right tests, and did it separate blocking issues from optional cleanup?

This matters for developer productivity because a useful review agent saves attention, not judgment. The goal is fewer missed risks and less repetitive scanning, not automatic approval.

The trap is measuring ai code review tools by comment count. A quiet receipt that says “no auth boundary touched, tests cover changed parser cases, one migration rollback risk remains” is better than twenty style comments.

Paste this review receipt into Cursor

Use this as a starter .cursor/rules/ai-review.mdc file. It gives Cursor Agent a repeatable review shape and gives your human reviewer something concrete to accept, reject, or improve.

---
description: Use for AI-assisted pull request review. The agent must review changed code without approving its own work.
globs:
  - **/*
alwaysApply: false
---

# AI review receipt

When asked to review a change, do not edit files unless explicitly asked.
Review only the files, tests, and docs relevant to the requested scope.

Return this receipt:

## Scope checked
- Files or directories inspected:
- Product behavior affected:
- Areas intentionally not reviewed:

## Findings
- Blocking issues:
- Non-blocking issues:
- Unclear assumptions:

## Tests and evidence
- Tests found:
- Tests missing or weak:
- Commands the human should run:

## Tool boundaries
- External context used:
- MCP tools used:
- Data or systems not accessed:

## Human decision
- Safe to merge: yes / no / unsure
- Reason:
- Follow-up owner:

Rules:
- Cite file paths when making a claim.
- Do not mark safe to merge when tests were not inspected.
- Do not approve code you generated in the same session.
- Say unsure when the evidence is incomplete.

Pair this with an AGENTS.md boundary such as: “Review agents may read code, tests, CI output, and linked tickets. They may not push commits, approve pull requests, modify secrets, or call write-capable MCP tools.”

Common questions

• What is the best LLM for code review for a team?

  The best LLM for code review is the one that follows your repo rules and produces reviewable evidence. Use a small evaluation set of 5–10 past pull requests with known defects, then compare receipts: file citations, missed risks, false positives, and whether the model says “unsure” when context is missing.

• Do we need multiple agents for code review?

  You do not need multiple agents until one review lane is reliable. Start with one reviewer that checks changed files, tests, and risky assumptions; add specialist agents only when the receipt shows a repeated gap, such as migrations, auth, accessibility, or API compatibility.

• Should the same agent write code and review it?

  Prefer a separate review pass when the agent wrote the patch. If the same coding agent must do both, require a clean handoff: implementation summary first, then a receipt that checks tests, risks, and assumptions without claiming independent approval.

• Where should team instructions live: AGENTS.md, CLAUDE.md, or Cursor rules?

  Put cross-tool repository rules in AGENTS.md, Cursor-specific behavior in .cursor/rules/*.mdc, and Claude-specific project memory in CLAUDE.md. Keep durable rules in files and keep one-off task details in the prompt, otherwise agents inherit stale instructions for months.

• How much MCP access should a review agent get?

  Give a review agent read-only access by default. A strong starter boundary is GitHub PR metadata, CI logs, linked issues, and internal docs; avoid write-capable tools unless a human explicitly invokes them for a separate task.

Further reading

• Cursor — Agent
• Claude Code — getting started
• OpenAI Developers — Codex quickstart
• Model Context Protocol — specification
• GitHub — openai/codex
• GitHub — anthropics/skills
• OWASP — Top 10 for Large Language Model Applications
• NIST — AI Risk Management Framework
• Google Search Central — helpful, people-first content
• Google Search Central — generative AI content guidance

Make the next review measurable

Pick one repo, add the Cursor rule above, and require the receipt on the next three pull requests. After that, tune the rule from real misses instead of guessing what your agents should know.

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.