AI code review and coding-agent verification solve related problems, but they do not produce the same kind of evidence.
Review searches a change for risks and explains suspicious code. Verification asks whether the requested behavior works in practice and preserves the evidence behind that verdict. When I am evaluating agent-written code, I 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 |
A reviewer can notice an authorization branch that looks unsafe. 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 useful because it tells me where to look and what to test. It should not be promoted into runtime proof.
A passing suite can be irrelevant to the requested task, stale, or incomplete. Verification makes test output stronger by binding it to the task, revision, environment, and expected behavior.
It also keeps unknowns explicit. If no check addresses a requirement, that requirement stays unverified instead of quietly becoming a pass.
CodeVetter is moving toward this combined evidence loop, with execution-backed verification as the authority. Its public recognition benchmark covers one narrow review dimension and explicitly does not claim to prove production pull-request performance.
The complete comparison and workflow are at https://codevetter.com/ai-code-review-vs-verification.