Choose an AI Code Review Workflow

There is no single best AI code review tool for every team. For AI code review, I would choose the tool that can review the right scope, show its evidence, respect your repository rules, and leave a receipt a human reviewer can check quickly.

The honest answer to “what is the best ai tool for code review” is usually a workflow answer, not a vendor answer. Strong code review tools can still miss risk if you ask them to review an entire branch without a plan, or if the reviewer accepts a confident summary without checking the changed files.

Start with the review job

Decide what the agent is allowed to review before you compare code review tools. A useful first scope is the pull request diff, the tests touched by that diff, and the local conventions that apply to those files.

In a normal GitHub pull request workflow, I ask the agent to compare the branch against main, list changed files by risk, then inspect only the highest-risk files first. That keeps the review bounded and makes it easier for a human to spot nonsense.

I teach this in the Review step of the methodology: the agent can help inspect work, but the team still owns the merge decision. That is also where code review guardrails belong, because the reviewer needs a repeatable way to ask for evidence.

Run the same review sequence each time

Use the same sequence across tools before you decide which code review AI fits your team. This is the procedure I would start with for an engineering team training on coding agents:

1. Ask the agent to identify the changed files and group them by behavior, data model, security, tests, and documentation.
2. Ask for a risk pass before a style pass.
3. Ask the agent to cite the exact file paths and line ranges it is relying on.
4. Ask it to propose tests only after it has described the behavior change.
5. Ask it to separate blocking findings from optional cleanup.
6. Have a human reviewer verify every blocking finding before requesting changes.

That sequence is deliberately boring. It reduces drift between reviewers, and it makes model quality easier to compare because each tool gets the same job.

Require a review receipt

A review receipt is a short record of what the agent checked, what it found, and what the human accepted or rejected. Without that receipt, llm code review becomes a chat transcript that nobody wants to replay during an incident.

Paste this into the pull request description or the review comment when an agent has helped with review:

## AI review receipt

Reviewer:
Tool or agent:
Branch compared against:
Commit or PR range:

## Scope checked
- Changed files reviewed:
- Tests inspected:
- Generated files excluded:
- External context used:

## Findings
| Severity | File | Finding | Evidence | Human decision |
|---|---|---|---|---|
| Blocking / Non-blocking | path/to/file | What the agent found | Line range, test, or command | Accepted / Rejected / Needs follow-up |

## Commands or checks run
- `command here`

## Open risks
- Risk:
- Owner:
- Follow-up issue or PR:

The receipt also helps when the agent used MCP to reach external systems. If an agent can read issues, docs, database schema, or CI status, the reviewer should be able to see which context was used and whether it was necessary. For a deeper pattern on making agent judgments easier to inspect, see typesafe-mcp Adds Typed Agent Judgments.

Know where the agent should stop

Do not ask the agent to be the final approver. It can find suspicious diffs, missing tests, unsafe data handling, inconsistent API behavior, and obvious security smells, but it does not carry production responsibility.

Security review needs extra care. OWASP’s work on large language model application risks is a useful reminder that agent access, prompt injection, data exposure, and tool permissions are part of the review surface, not side issues.

There is also a cost tradeoff. A broad agent review over a large branch can burn time and tokens while producing vague comments, so I prefer smaller review scopes and a clear receipt over one giant pass.

Further reading

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

Try it on one pull request

Pick one active pull request, run the six-step sequence, and attach the review receipt before asking anyone to change their normal approval rules.