# AI code review vs. coding-agent verification

> Canonical page: https://codevetter.com/ai-code-review-vs-verification

AI code review and coding-agent verification solve related but different problems. Review searches a change for risks and explains suspicious code. Verification determines whether the requested behavior works and preserves evidence for the verdict. Teams using coding agents usually need both.

| Question | AI code review | Agent verification |
| --- | --- | --- |
| Primary input | Diff and repository context | Task, exact change, environment, and checks |
| Main output | Findings and explanations | Pass, fail, or unverified with evidence |
| Strong at | Breadth, suspicious patterns, maintainability clues | Reproducing behavior and proving closure |
| Main limitation | A plausible finding may not reproduce | A check can miss risks outside its behavioral boundary |
| Best use | Risk discovery and reviewer focus | Acceptance, regression protection, and auditability |

## Why review alone is not enough

A reviewer can notice an authorization branch that looks unsafe, but only an authoritative check can show whether an unauthorized request is accepted. A model can praise a state update while a browser journey still loses user input. Review remains valuable because it can propose where to test; it should not be promoted into runtime proof.

## Why tests alone are not enough

A green suite can be irrelevant to the requested task, stale, or incomplete. Verification strengthens test output by binding it to the task, revision, environment, and expected behavior. It also preserves explicit unknowns instead of treating an absent check as a pass.

## A practical combined loop

1. Review the diff and repository context to identify risk.
2. Translate material risks and acceptance criteria into focused checks.
3. Run repository-owned tests plus the smallest missing behavioral check.
4. Package findings and execution results separately.
5. Re-run after fixes and retain the before-and-after evidence.

CodeVetter's current direction is this combined evidence loop, with execution-backed verification as the authority. The public recognition benchmark remains useful for one review dimension and openly states that it does not prove production PR performance.

**CTA:** Start with the verification workflow, then inspect the public benchmark and its limitations.

## Public product links

- [CodeVetter](https://codevetter.com/)
- [Download](https://codevetter.com/download)
- [Documentation](https://codevetter.com/docs/)
- [Source](https://github.com/Codevetter/codevetter)