AI-Assisted Code Review on Mac: A Reviewer-Controlled Workflow

AI-assisted code review on a Mac works best when the agent helps you read the change, not when it publishes a second review without your approval.

That sounds like a small difference. It changes the whole workflow. The agent can trace a call path, explain an unfamiliar function, or look for an edge case. You check the result in the local workspace. Then you decide whether a comment is accurate, useful, and worth sending to the author.

This guide lays out a practical workflow for using AI during pull request review on macOS without filling the thread with guesses.

Why automatic AI review comments create work

Most AI code review tools are built around one visible output: a comment on the pull request. Every comment then becomes someone else's task.

Is the finding correct? Is the behavior intentional? Is it already handled by another layer? Does the author need to act on it in this pull request?

A long list of plausible comments slows the review down. False positives make authors defend code that was fine. Reviewers learn to scan past automated feedback. The tool may find more things, but the team understands less.

AI should not get a direct path from “I found something” to “the author must respond.” Put the reviewer between those two steps.

A better model: AI as a reading assistant

Use the agent for questions that involve investigation. Keep the final judgment with the person reviewing the change.

Good prompts include:

• “Trace this value from the changed function to its callers.”
• “What happens when this input is missing or invalid?”
• “Compare this new path with the existing implementation.”
• “Which tests cover the behavior changed in this hunk?”
• “What should I verify before approving this API change?”

These prompts help you build a model of the change. They do not ask the agent to decide whether the pull request should be approved.

A local workspace makes the answers more useful. The agent and the reviewer can inspect the code around the diff instead of treating changed lines as an isolated document. The question becomes “what does this change affect in the repository?” rather than only “does this line look suspicious?”

Keep every AI finding as a draft

The safest AI comment is one you have read before the author sees it.

When the agent finds a possible issue, keep the suggestion tied to the relevant line as a draft. Then choose one of three outcomes:

• Keep it. The finding is correct, relevant, and clear enough to help the author.
• Discard it. The finding is wrong, already handled, or not worth interrupting the review for.
• Rewrite it. The agent found a useful lead, but you need to add the system or product context.

The third option matters. A possible issue may be intentional. A later layer may validate the value. The pull request may be part of a migration. An agent can point at the code, but it does not own the trade-off.

A step-by-step AI review workflow on macOS

1. Open the pull request in a local workspace

Start with the branch that is being reviewed. You want the changed code, its dependencies, and the current project state available together.

Do not confuse local review with downloading a diff. A diff without the surrounding repository is still a partial view. The local workspace is what lets you check definitions, callers, configuration, and tests on the same branch.

2. Read the diff before asking the agent

Make your own first pass. Identify the purpose of the change, the files it touches, and the parts you do not understand yet.

This keeps the agent focused. It also prevents a generated summary from replacing your understanding of the pull request.

3. Use code navigation for basic context

Jump to definitions, inspect types, and find references from the changed code. Many questions are faster to answer with navigation than with a generated explanation.

This is where a native macOS review surface helps. You can move from a hunk to the surrounding code and return to the same review location without rebuilding your context across browser tabs.

4. Ask AI about the risky or unclear parts

Use the agent for edge cases, failure paths, unfamiliar code, and changes that cross several files. Ask one focused question at a time.

For example, if a permission check moved, ask the agent to trace the affected paths and list the callers that rely on the old behavior. Then inspect those paths yourself.

5. Check the suggestion against the code

Never keep a comment because it sounds specific. Follow the path. Check the types. Read the tests. Look at the code outside the diff when the answer depends on it.

AI can miss conventions, hidden invariants, generated code, and decisions made elsewhere in the system. Its output is a lead, not evidence by itself.

6. Keep only comments that help the author

A review comment should describe a real problem, ask a concrete question, or point to a change that will make the code safer. Do not publish a note just because the agent found something plausible.

Why a native Mac review app can help

A browser is fine for opening a pull request. Large reviews ask for more movement between the diff and the codebase.

You need to switch between files, definitions, references, tests, and comments without losing your place. Keyboard navigation reduces the pointer work. A local clone provides the code context. LSP support connects symbols in the diff to the rest of the project.

That changes the role of AI. You do not need an explanation for every line because navigation answers many basic questions directly. You can save the agent for investigation and edge cases. The result is less generated commentary and more time spent on the actual design.

What to check when choosing an AI code review tool

• On-demand use. You can ask about a file, hunk, selection, or behavior instead of receiving a comment flood.
• Draft-first suggestions. Findings are editable and rejectable before they reach the author.
• Local code context. The tool can inspect the workspace around the changed lines.
• Symbol navigation. Definitions, types, and references are available during review.
• Stable review state. You can return to the right file and hunk after investigating a question.
• Human-controlled submission. The reviewer decides which comments and final review state are sent to GitHub.

AI should reduce hunting, not reduce thinking

The goal of AI-assisted code review on Mac is not to approve more pull requests without reading them. It is to spend less time hunting through unfamiliar code and more time checking whether the change is safe and intentional.

Use local navigation to understand the system. Use AI to investigate the parts that need extra work. Keep suggestions as drafts. Then make the call yourself.

Relu is built around that workflow. It is a native macOS app for reviewing GitHub pull requests, with a local workspace clone, LSP navigation on the diff, and an on-demand AI agent whose suggestions stay drafts until you choose to keep them. See how Relu handles code review.

For the navigation side of the workflow, read why pull request review still needs go-to-definition. For a keyboard-first review loop, see this practical guide to keyboard-driven code review on macOS.