Choose a Code Review Model

No single model is the right reviewer for every pull request. For AI code review, choose the smallest reliable workflow first, then pick the model that can follow it consistently on your codebase. I use the same order in AI coding training because teams get better results from clear review jobs, risk routing, and receipts than from swapping models every week.

Start with the review job

The best LLM for code review is the one that can do a named job with evidence. Do not ask a model to review everything. Ask it to check one class of risk: security, data shape, tests, API compatibility, or migration safety.

That also makes model choice less emotional. A fast model may be enough for naming, dead code, obvious test gaps, and small diffs. A stronger model is worth using when the diff crosses trust boundaries, changes authorization, touches money, or rewrites concurrency.

Route reviews by risk

Use a short routing rule before you open any code review AI tool. As of October 2026, model quality still moves quickly, so a team rule should describe the risk class and expected evidence, not a permanent winner.

A workable decision list looks like this:

1. Use a fast pass for small, local diffs with tests already changed.
2. Use a deeper pass for authentication, billing, migrations, data deletion, permissions, dependency updates, or public API behavior.
3. Use a human reviewer as the owner for high-risk code, with the model acting as a second reader.
4. Reject agent comments that do not point to a file, line range, test, trace, or reproducible failure.
5. Re-run the review after the author changes the PR, because the second diff is often where the bug enters.

A concrete workflow: a GitHub PR changes packages/api/src/routes/billing.ts, a Prisma migration, and a webhook test. I would route that to a deeper LLM code review because it mixes external input, money movement, and schema state. I would not let the model approve it. I would ask for a receipt that the human reviewer can check quickly.

Make the agent leave evidence

A useful review comment is not a vibe. It should show what the agent inspected, what it thinks can break, and what proof would change its mind.

This is the Review step in our methodology: delegate the scan, review the evidence, then keep ownership with the engineer. For teams turning agent judgments into typed or callable tooling, typesafe-mcp Adds Typed Agent Judgments is a useful adjacent pattern.

The receipt matters more than the prose. Without it, code review tools create extra comments but not extra confidence.

Keep tools on a short leash

OWASP’s Top 10 for LLM Applications is a useful boundary for review agents because these agents can read code, call tools, and influence merge decisions. The issue is not only model accuracy. It is what the model can reach and what the team treats as authoritative.

If you connect the reviewer through MCP, start with read-only access. MCP is the common integration layer many teams use to let agents call external tools and read resources. For code review, that usually means GitHub, CI logs, test results, issue trackers, and documentation, not production write access.

A plain rule works: the review agent may inspect, summarize, and suggest. It may not merge, label a PR as safe, rewrite protected files, or change policy without a human owner.

Paste this review receipt into the PR

## AI review receipt

PR:
Reviewer model:
Review date:
Commit reviewed:

## Review scope
- Files inspected:
- Files ignored:
- Commands or logs inspected:
- External context used:

## Risk routing
- Risk level: low / medium / high
- Reason for this level:
- Human owner for final decision:

## Findings
- Finding:
  - File and line:
  - Risk:
  - Evidence:
  - Suggested fix:
  - Test that should fail before the fix:

## Non-findings
- Areas checked where no issue was found:
- Assumptions the reviewer made:

## Merge recommendation
- Do not merge yet / safe after listed fixes / no blocking findings
- Reason:

Keep the receipt in the PR description or as the first AI review comment. If the model cannot fill in the evidence fields, the review is not ready to influence the merge.

Further reading

• Cursor Agent docs
• Claude Code docs
• Codex quickstart
• OWASP Top 10 for Large Language Model Applications
• MCP specification

Run it on one pull request

Pick one medium-risk PR this week, route it with the decision list, and require the review receipt before the AI feedback can affect the merge.