AI code review and agent verification are not the same

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

Why review alone is not enough

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.

Why a green suite is not enough either

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.

The combined loop I use

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 review findings and execution results separately.
5. Re-run after fixes and retain the before-and-after evidence.

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.