AI Code Review Checklist: What to Automate vs Block

You can now get a working pull request in minutes, but you still cannot get a working approval that fast. That gap is the real bottleneck in AI-assisted development, and most teams are still trying to close it with the wrong tool: more careful line-by-line reading.

The fix is not a slower review. It's a different review, one that spends human attention in proportion to blast radius and rollback cost, not generated line count. Below is a checklist you can adopt this week, split into what your linters and CI should own outright, what a second AI reviewer agent can competently check, and what should never ship without a human signature.

The Blast-Radius Standard Replaces Line-by-Line Review

Line-by-line review made sense when a person wrote every line slowly, after already making a hundred small judgment calls before typing it. Generated code arrives without that filtering. A model asked to build a feature with several edge cases will often produce a ladder of if-statements, feature flags, and fallback branches, one for every condition you mentioned, because it has no roadmap or prior argument telling it which constraints deserve a durable abstraction and which are just a passing requirement.

That is not a formatting problem. It is a legibility problem. The question a reviewer needs to answer is not "is this pretty" but "can I explain why this branch exists, and what happens if it's wrong."

A change that touches a marketing page and a change that touches your payment webhook can both look like 80 lines of diff. They do not deserve the same review.

Once you accept that framing, the checklist writes itself. Sort every incoming change into one of three buckets before you read a single line of logic: mechanical issues that tools should catch, structural and intent issues that a second AI agent can flag, and boundary decisions that require a human who can be held accountable.

Tier One: What Linters and CI Should Own Completely

Anything a deterministic tool can catch should never occupy a human reviewer's attention, and it should rarely occupy the first AI reviewer's attention either. If your pull requests still generate comments about these items, your tooling is under-configured, not your team under-disciplined.

• Formatting, import order, and unused variables: enforce with a fast formatter and linter (Biome, ESLint with strict rules, or your language's equivalent) that runs on save and blocks merge on failure.
• Type errors and null-safety violations: TypeScript's strict mode, or your language's static analyzer, should reject the build before a human ever opens the diff.
• Dead code and unreachable branches: static analysis tools flag these reliably; a reviewer scanning a 300-line diff will miss half of them.
• Test coverage deltas on touched files: a CI gate that fails when new logic ships without new or modified tests removes an entire category of "did they even test this" comments.
• Dependency and vulnerability scanning: automate it on every PR instead of discovering problems three weeks later in a security audit.

If a reviewer's comment could have been written by a script, it should have been. Every minute spent flagging a missing semicolon is a minute not spent asking whether a permission check is missing. See related: beyond the 100x engineer: rethinking ai adoption for additional background.

Tier Two: What a Second AI Reviewer Agent Should Catch

This is the tier most teams skip, and it's the one doing the most new work. The idea is simple: the model that wrote the change should not be the only reviewer of the change. A second agent, running with an independent prompt and ideally a different model or fresh context, reads the diff against the ticket, the touched files, and the existing test suite, then reports back before a human looks at anything.

A well-configured AI reviewer agent should reliably flag:

• Scope creep relative to the ticket. If the ticket asked for a discount code field and the diff adds a new eligibility engine with five conditions, that mismatch should surface automatically, instead of turning up three review cycles later.
• Duplicated business rules. If a React component and a Next.js API route both independently encode "users under 18 cannot book," the agent should flag the duplication as an ownership problem, even if each copy works.
• Expanded permissions or widened access. If a change quietly loosens an authorization check, adds a new admin-only route without a guard, or exposes an internal field in an API response, the agent should call it out by name instead of leaving it for a human to notice on a tired Friday afternoon.
• Missing or mismatched tests. Not just "are there tests," but "do these tests actually exercise the edge cases the ticket described."
• Unexplained control flow. If the agent itself cannot produce a plain-language explanation for why a branch exists, that is a signal the human reviewer will not be able to either.

This tier works because the agent has time and patience a human reviewer doesn't. It can trace every caller of a modified function, diff behavior against the previous version, and cross-reference the ticket text, all before your morning coffee. Treat its output as a triage report, not a verdict. The human still decides what matters.

Tier Three: What Always Requires Human Sign-Off

Some changes get the strict standard no matter how clean the diff looks. This is the category where telling yourself a future model can simplify it later is not a rollback plan, because the cost of a bad decision compounds faster than the cost of ugly code. For more on this, see a closer look at claude ai finds crypto implementation flaws in tls, ssh.

Authentication and session handling. A token refresh flow, a password reset endpoint, or a change to how sessions get invalidated needs a human who can trace every caller and every failure mode end to end. An AI reviewer can flag that a new code path skips a check. Only a person can decide whether skipping that check is ever acceptable.

Payments and billing logic. A retry policy on a failed charge, a change to how refunds get calculated, or new logic in a webhook handler for a payment provider all sit in this tier. The failure modes are financial and reputational, and they often stay invisible until a customer complains weeks later.

Database migrations and schema changes. Renaming a column, backfilling a field, or changing a foreign key constraint touches data you cannot regenerate. A migration that looks correct in a diff can still corrupt production data if the rollout order is wrong or a lock takes longer than expected under load.

Shared infrastructure and platform code. Changes to a shared authentication middleware, a rate limiter, a logging pipeline, or a core API client used across services carry blast radius that a single ticket description cannot capture. The person approving it needs institutional knowledge of every consumer, not just the one that prompted the change.

Notice what these four have in common: they accumulate callers, permissions, and operational assumptions that a future rewrite cannot undo. A cleaner abstraction next quarter does not un-leak a password hash or un-corrupt a customer's balance today.

Also read: related topic: how llms reward expertise: the technical edge

The Decision Matrix in Practice

Here is how the three tiers map onto real pull requests.

| Change type | Sensible bar | Block when | |---|---|---| | Internal admin dashboard filter | Behavior is tested; easy to roll back | Control flow can't be explained in one sentence | | Marketing page A/B test component | Works, has basic tests, flag is documented | The flag logic leaks into shared components | | Discount or eligibility rule in checkout | Intent and edge cases are explicit, ownership is clear | Rule is duplicated across frontend and backend | | Database migration adding a nullable column | Reviewed for lock behavior and rollback script | Rollback plan is undefined or migration is irreversible | | Payment webhook retry logic | Failure modes and idempotency understood end to end | Retry behavior could cause duplicate charges | | Auth token refresh change | Every caller and expiry path traced | Permission scope silently widens |

The pattern across every row is the same. Cheap-to-reverse code gets a fast, working-code-first standard, because a future model or a future you can clean it up once real usage tells you which branches matter. Expensive-to-reverse code gets the old, slow standard: understand it, challenge the abstraction, reject anything that isn't earned.

Most teams already sense this distinction informally. They just haven't written it down, so every reviewer applies a different threshold, and every review turns into a negotiation about taste instead of a decision about risk. Writing the matrix down, and pointing to it in a pull request template, turns that negotiation into a checklist.

The teams getting real speed from AI-generated code aren't the ones reviewing every diff more carefully. They're the ones who moved formatting and type-safety into tools, moved scope and duplication checks into a second AI reviewer, and reserved human judgment for the handful of changes where a bad call is expensive to undo. That reallocation, not a faster model, is what actually shortens the path from prompt to production.