Give the reviewer a finite contract, not an invitation to keep brainstorming. Supply the current diff and the interfaces it can affect; require each finding to identify a location, explain a concrete failure, and cite evidence from code, tests, or execution. Validate high-severity claims, merge duplicate reports, and stop when no blocking finding remains and deterministic CI passes. Record lower-risk observations for later maintenance instead of reopening the same review indefinitely.
A later pass can legitimately discover something new because it sees more repository context, a changed diff, or execution evidence that was unavailable before. That does not automatically mean the earlier pass was incompetent. The practical control is an explicit stopping rule and a minimum evidence threshold.
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Why an AI reviewer keeps producing “one more” error
AI review is not a single deterministic lint command. The model may inspect different portions of the repository on different passes, receive a revised diff after each fix, or gain new information from tests and execution. A first pass that examines only changed lines can miss an interaction with a shared interface. A later pass that reads the caller, configuration, or test fixture may identify a real issue that was previously outside scope.
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Use a review contract that answers four questions before the model starts:
- What exact revision and files are in scope?
- Which interfaces, data flows, and security assumptions are affected?
- What evidence must a finding contain before it can block the change?
- What condition ends the review?
Those answers turn “find everything imaginable” into “find actionable, evidenced defects in this change.”
Write a finite review contract
Define the scope in the prompt or repository policy
Start with the commit range or pull-request diff, then name the explicitly affected interfaces: public APIs, database schemas, message formats, authorization boundaries, file formats, and background jobs. Ask the reviewer to inspect the smallest amount of surrounding code needed to verify those interfaces. Do not request a repository-wide style audit during a correctness review.
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Set an evidence threshold
Require every finding to include all of the following:
- Location: file path and line or the smallest identifiable code region.
- Affected behavior: what the program does differently or can now do incorrectly.
- Failure scenario: an input, state, permission, race, or deployment condition that triggers the problem.
- Evidence: a failing test, reproducible command, execution trace, direct code path, or documented contract that proves the concern.
- Severity: the agreed level and why it crosses the blocking threshold.
- Remediation: a focused fix, not an unrelated refactor.
“This might be unsafe” is a hypothesis, not a blocking finding. Ask the model to label unverified hypotheses as observations and keep them out of the merge gate.
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Specify what is not part of this pass
Explicit exclusions are as important as inclusions. State that the pass is not for naming preferences, broad refactoring, hypothetical future requirements, or unrelated legacy defects. If a concern is outside the diff but genuinely important, the reviewer should file it as a follow-up item with a separate owner rather than reopening the current review.
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A review protocol that ends
Use the following sequence for each pull request. It is deliberately finite; a new pass is justified only when the diff or evidence changes.
- Freeze the review input. Record the commit SHA, diff, target branch, build configuration, and the affected interfaces. If the author pushes new code, begin a new pass against the new SHA rather than blending comments from different revisions.
- Run Pass 1 on the diff. Ask for correctness, security, and test-impact findings only. Require the evidence fields above and prohibit style brainstorming.
- Validate high-severity findings. For each potentially blocking issue, request a minimal failing test, a reproducible command, or an isolated execution trace. OpenAI’s Codex Security documentation describes identification, validation, and remediation; validation attempts to reproduce a potential vulnerability in an isolated environment to reduce false positives.
- Deduplicate by root cause. If five lines fail because one missing authorization check controls them all, record one finding at the controlling check. Anthropic documents deduplication and severity ranking; use those controls so one defect cannot create a page of near-identical blockers.
- Apply the severity gate. Fix or explicitly dismiss every high-severity item. Decide in advance whether medium findings block this pull request. A common policy is to block only issues that can cause a security compromise, data loss, outage, incorrect financial result, or contract-breaking behavior.
- Run deterministic checks. Execute the project’s CI checks, targeted tests, static analysis, dependency and secret scans, and any reproduction created for a finding. OWASP recommends running SAST, SCA, and secret scanning on every pull request, while treating a clean scan as the start of review rather than its end.
- Close the pass. Stop when the reviewed diff is covered, all blocking findings are resolved or documented, the agreed validation step produces no new blocking evidence, and CI is green. Log lower-severity observations in the issue tracker without asking the model to reconsider the same diff.
A later review is warranted when the author changes behavior, a test exposes new evidence, a dependency or configuration changes, or a previously documented assumption is invalidated. It is not warranted merely because the model can generate another hypothetical.
Make behavioral validation mandatory
Ask for a minimal reproduction
For a suspected bug, request the smallest input and command that demonstrates it. For example, a reviewer claiming that an authorization check is bypassable should identify the request, authenticated role, expected response, actual response, and test or trace that demonstrates the path. A claim that a parser crashes on malformed data should include the malformed payload and the observed exception or failing assertion.
When reproduction is impossible in production, use an isolated fixture or a narrowly scoped unit test. State the environment assumptions: runtime version, feature flag, database state, and network availability. If the model cannot produce a credible reproduction or code-level proof, classify the item as an unverified observation rather than a defect.
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Static reasoning can establish that a value is unsanitized at a sink or that a null path is unhandled. Execution evidence can establish whether that path is reachable under supported inputs. Require the reviewer to say which type it used. This distinction prevents a plausible data-flow story from being presented as a demonstrated exploit.
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Do not let a passing scan erase review responsibility
OWASP’s guidance is explicit that AI-assisted review tools can summarize diffs and flag patterns, but a human with accountability must still approve security-relevant changes. Human attention should focus on authorization, business logic, data flows, threat-model assumptions, and whether a proposed fix changes behavior outside the diff. A clean SAST or secret scan is useful evidence; it is not approval by itself.
Control precision, severity, and duplicate comments
Optimize for precision, not comment volume
OpenAI’s code-verification guidance states that precision is more important for usability than recall. A reviewer that reports fewer, well-supported findings is more useful than one that lists every conceivable concern. Configure prompts and gates accordingly: require evidence, cap the number of blocking findings per root cause, and allow “no actionable finding” as a valid result.
Repository-wide context and execution access can improve both recall and precision, but only when the additional context is relevant. Give the model the caller, callee, schema, or test it needs to verify behavior; do not automatically expose every unrelated directory and then treat speculative output as a defect.
Use a severity rubric that maps to action
| Severity | Evidence expected | Pull-request action |
|---|---|---|
| High | Reproducible failure, exploitable path, data-loss scenario, or contract break tied to the diff | Blocks merge until fixed or explicitly accepted by the accountable owner |
| Medium | Concrete defect with a credible path, but limited impact or a compensating control | Block only if project policy says so; otherwise assign a dated follow-up |
| Low | Maintainability or edge-case concern without demonstrated material impact | Record for normal maintenance; do not reopen the same review |
| Observation | Hypothesis lacking a reproduction or code-level proof | Do not block; request evidence or close as unverified |
Severity is a decision, not a synonym for confidence. A highly confident naming suggestion is still non-blocking; a lower-confidence security hypothesis may deserve a validation task before the pull request closes.
Turn recurring findings into engineering controls
Repeated AI comments usually indicate a missing control, not a need for more review passes. OWASP’s review guidance says: “No feedback loop — the same issue found repeatedly in review should become a lint rule, library helper, or training topic.” Choose the control that prevents the category at its source.
- Lint rule: enforce a forbidden API, unsafe interpolation pattern, error-handling requirement, or naming convention automatically.
- Library helper: centralize escaping, authorization, retry, or transaction behavior so individual pull requests cannot reimplement it inconsistently.
- Test: add a regression test for the exact failure scenario and run it in CI.
- CI gate: run SAST, SCA, secret scanning, schema checks, or policy validation on every pull request.
- Training topic: document a domain-specific mistake when the issue depends on judgment rather than syntax.
After adding a control, update repository guidance so the reviewer knows the rule exists and should not report the old pattern as a new discovery. Keep the original issue linked to the control’s change for auditability.
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Handle accepted exceptions without restarting the loop
Not every finding should be fixed. GitHub documents dismissing findings for legacy code, known exceptions, and false positives that do not pose a real quality risk. A dismissal is safe only when it records the reason, scope, owner, and any condition that would make the exception invalid.
Use a short exception record such as: “Accepted because the gateway authenticates this route before traffic reaches the service; integration test gateway_auth_required covers the boundary; revisit if the route is exposed directly.” If the same exception appears repeatedly, encode it in repository guidance or the scanner’s rule configuration. Do not rely on the model remembering a comment from an earlier pull request.
Keep humans accountable for the decisions AI cannot own
An AI reviewer can inspect code consistently, summarize a diff, and suggest tests. It cannot accept business risk on behalf of your organization. The accountable human reviewer must decide whether authorization is appropriate, whether a data flow matches the threat model, whether a migration is reversible, and whether a fix changes behavior beyond the stated scope.
Require the human approver to acknowledge three things in security-sensitive changes:
- The blocking findings were fixed or explicitly accepted by an identified owner.
- Validation and CI ran against the final commit, not an earlier revision.
- Business-logic and threat-model assumptions were reviewed, even when automated checks passed.
This division of labor prevents two opposite failures: trusting every AI comment as a defect and treating a quiet AI pass as proof that the change is safe.
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Do not compare reviewers by the number of comments they produce. Compare whether they help you reach a defensible stopping point.
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| Capability | Questions to ask | Why it matters |
|---|---|---|
| Behavioral verification | Can it run a test, reproduce a finding, or show an execution trace in an isolated environment? | Separates demonstrated failures from plausible speculation. |
| Repository context | Can it read versioned guidance, ownership rules, interfaces, and relevant callers without scanning unrelated code? | Applies local policy and limits irrelevant comments. |
| Deduplication | Does it merge comments that share one root cause? | Prevents one defect from becoming many blockers. |
| Severity controls | Can your team define which levels block and how confidence is represented? | Aligns output with risk instead of model enthusiasm. |
| CI integration | Does it run on the final commit alongside deterministic tests, SAST, SCA, and secret scans? | Ensures the decision is based on reproducible checks. |
| Dismissal auditability | Can reviewers record why a finding was dismissed and who accepted the risk? | Preserves accountability and prevents recurring debates. |
| Human approval points | Does the workflow require accountable approval for security-relevant or business-critical changes? | Keeps risk acceptance with the people authorized to make it. |
Anthropic documents an average completion time of 20 minutes for its Code Review workflow; that figure is product documentation, not an independent benchmark. OpenAI’s 2026 Auto-review report describes roughly 200 times fewer human approval interruptions and around 99% of reviewed actions approved by Auto-review. Those figures describe the named products and their reported conditions, not a universal performance guarantee. The useful question for your team is whether fewer interruptions come from better evidence and policy or simply from lowering the approval bar.
Troubleshoot endless review churn
The model finds a different style issue after every fix
Split style from correctness. Disable style comments for the blocking pass, enforce formatting with the project’s formatter, and run a separate non-blocking cleanup when desired. Put accepted style choices in repository guidance.
The same security concern returns in every pull request
Find the common root cause and create a lint rule, helper, regression test, or CI gate. Add the exception or safe pattern to guidance. A repeated comment without a new control is a process failure, not evidence that every pull request needs another open-ended review.
Fixing one finding creates two more
Review the new diff as a new revision, but preserve the original contract. Ask the model to compare the behavior before and after the fix, run the regression test, and report only regressions introduced by that change. Do not carry unrelated observations forward as blockers.
The reviewer reports issues outside the changed code
Ask for the exact interface through which the diff changes reachability. If no such path exists, move the concern to a separate issue. If the path does exist, expand scope deliberately and document why.
Every finding is marked high severity
Require impact criteria and a concrete failure scenario. A high label without demonstrated impact should be downgraded or treated as an observation. Have the human owner approve the rubric and sample findings so the model’s labels remain consistent.
A prompt pattern you can adapt
Review commit <SHA> against <BASE>.
Scope: changed files plus the callers and interfaces listed below: <list>.
Check correctness, security, and test impact only; do not suggest style or unrelated refactors.
For each finding provide: file/line, affected behavior, concrete failure scenario, evidence,
severity (high/medium/low), and a focused remediation.
A finding blocks only when the evidence demonstrates material impact under supported conditions.
For high-severity findings, produce a minimal failing test, reproducible command, or isolated trace.
Merge comments with the same root cause. Mark unverified hypotheses as observations.
Stop after the final validation command and report “no actionable findings” when none remain.Replace the placeholders with the actual commit, base branch, interfaces, and validation commands. Store the durable portions in versioned repository guidance so every reviewer receives the same policy.
When exactly should you run another pass?
Run another pass only after one of four material changes: the diff changed, the affected interface or configuration changed, a test or execution trace supplied new evidence, or a previously accepted assumption was withdrawn. Start from the new commit and state what changed. If none of those conditions applies, another pass is more likely to add noise than safety.
The review is complete when the final diff has been examined under the agreed scope, blocking findings are resolved or documented by an accountable owner, required validation and CI checks pass, and no new blocking evidence appears. That is a defensible engineering decision; waiting for an AI to become incapable of imagining another concern is not.
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