AI Agent for Code Review: Evidence

An AI review agent is a repository process with engineering ownership. It can inspect a pull request before a reviewer, apply written checks and collect evidence. The reviewer decides whether the change is correct for the product and safe to merge.

Signals for the first review pass

Choose checks with a rule, a repository location and an observable result:

• naming and formatting conventions;
• unused imports, dead code and unreachable branches;
• missing tests around changed behavior;
• error paths that the repository treats as required;
• dependency or configuration changes that need a named owner;
• input handling and secret exposure signals that deserve security review.

The agent should link each finding to a file, line or test result and explain the rule that triggered it. A reviewer can then confirm, dismiss or route the finding.

Repository context and access

Write an access table before connecting the agent:

Keep tokens narrow and make every tool call visible in the run record. A review agent should operate on the pull request context required for its checks.

Finding severity and evidence

Define the finding states before the first run:

• blocking candidate: a rule or security owner requires a human check before merge;
• review candidate: evidence suggests a defect or missing test;
• informational: a convention or maintainability note;
• dismissed: the reviewer recorded why the finding does not apply.

The agent can propose a state. A human reviewer confirms severity and disposition. Track repeated false alarms by rule so the team can narrow or rewrite checks.

The human review boundary

The reviewer owns architecture, domain behavior, API contracts, data migrations, authorization, security impact and release risk. A passing agent run carries no approval by itself. Branch protection, code owners and CI should keep merge authority with the team’s existing controls.

Pilot on one repository

Select a repository with a stable test command and an owner who will review every finding. Run the agent in a shadow lane first, compare findings with normal review and record the reason for each disposition. Then enable comments on the pull request while preserving the same merge controls.

Review the agent after rule, dependency, model or repository changes. Retain the run record long enough to investigate a disputed finding, with access and deletion rules documented by the repository owner.

Measures for a useful review lane

Use the team’s own review records:

• findings accepted, dismissed and edited by reviewers;
• repeated findings by rule and repository area;
• review time spent on the covered checks;
• defects found after the pull request reached a later environment;
• runs that failed because context, tests or permissions were missing.

These measures show whether the lane reduces repetitive review work. They do not establish that the agent can certify a release.

Choose the first review lane

Start with mechanical checks and evidence collection in one repository. Add domain-sensitive checks only when a named owner can write the rule, inspect its evidence and accept the false-alarm cost.

Discuss a controlled code-review lane with Syntalith if you want to map repository access, checks and reviewer ownership.