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Stop the loop by giving your AI reviewer a written contract: review the current diff and named interfaces, report only actionable findings with a location and reproducible evidence, deduplicate root causes, and block only the agreed severity. The review is finished when every high-severity finding is fixed or explicitly accepted, deterministic CI and targeted tests pass, and the agreed validation step produces no new evidence. Lower-severity observations become a maintenance queue instead of a reason to reopen the same pull request indefinitely.
A later pass can legitimately find something new when it sees broader repository context, a changed diff, or execution evidence that was unavailable earlier. That does not by itself show that the first pass was incompetent. The practical control is a stopping rule plus an evidence threshold.
Why an AI reviewer can find a “new” error on every pass
AI review is not a single, fixed inspection. What the reviewer can see and verify changes with each pass. Three changes commonly explain a new comment:
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- A changed diff: A fix, rebase, generated file, or conflict resolution can alter behavior. The second review is then examining a different program.
- Execution evidence: A command, test, sandbox run, or trace can expose behavior that static reading could not establish. A potential issue that lacked proof in pass one may become verifiable in pass two.
These are changes in information, not proof that every new comment is valid. Without a contract, the reviewer tends to turn every possible concern into a blocking comment, and developers respond by rerunning the review. The result is pull-request churn rather than better software.
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Write the review contract before asking for another pass
A finite contract tells the reviewer what to inspect, what counts as a finding, and when to stop. Put the contract in the pull-request instructions or the repository’s review guidance so it is applied consistently.
Define the inspection boundary
State the commit or diff being reviewed, the directly affected interfaces, and the behavior that must remain compatible. Include architecture rules, security assumptions, and project-specific “do not flag” conventions. Anthropic’s Code Review documentation describes repository guidance files for this purpose and notes that reviews can be triggered after every push. Local guidance prevents generic preferences from overruling the project’s actual policy.
For a narrowly scoped change, name the API endpoints, data models, command-line paths, jobs, or user roles that can be affected. If a generated file or vendored dependency is intentionally excluded, say so. A reviewer cannot make a meaningful stopping decision when the scope silently expands from one patch to the entire repository.
Set an evidence threshold
Require every reported issue to contain all of the following:
- File and line, or another precise code location.
- The affected behavior and the severity.
- A concrete failure scenario, including the input, state, or actor that triggers it.
- Evidence from code, a test, a reproducible command, or an execution trace.
- The smallest practical remediation, when a fix is clear.
“This might be unsafe” is a lead for investigation, not a completed finding. If the reviewer cannot identify a reachable path and a plausible consequence, it should label the item as an unverified observation or omit it rather than block the change.
Choose the blocking severity
Agree in advance which severities stop the pull request. A useful default is to block high-severity, actionable defects; repair medium-severity items when they are in scope; and record low-severity observations for maintenance. The exact labels can follow your existing taxonomy, but the decision must be explicit. Otherwise every reviewer comment becomes an argument about whether another pass is required.
Specify the stop condition
Stop after the current diff is covered, every high-severity item is fixed or documented as an accepted exception, deterministic CI and targeted tests pass, and the agreed validation step has produced no new evidence. Do not reopen the same pass merely because the model can imagine another possibility. A new review is justified only by a changed diff, newly supplied context, or new execution evidence.
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Give the reviewer project policy, not just source code
Repository guidance is the place for rules that are otherwise easy for a model to miss:
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- Authentication and authorization boundaries, including which service is the source of truth.
- Data-classification and retention assumptions.
- Concurrency, retry, idempotency, and backward-compatibility requirements.
- Security controls that are deliberately centralized in a library or middleware.
- Patterns that are acceptable in tests, migrations, generated code, or legacy modules.
- Findings the team has decided not to report, with the reason and the safer alternative.
Keep guidance versioned with the repository. When a rule changes, the review contract changes with it. This is more reliable than trying to correct the model conversationally after it has produced a long list of generic style comments.
Validate behavior before accepting a bug claim
OpenAI’s Codex Security documentation describes a sequence of identification, validation, and remediation. Validation attempts to reproduce a potential vulnerability in an isolated environment, which is intended to reduce false positives and keep findings high-signal. Apply the same principle to correctness and reliability findings.
Use the smallest useful proof
Ask for one of these forms of evidence:
- A minimal failing test that demonstrates the incorrect result.
- A reproducible command with the relevant input and expected versus actual output.
- An isolated execution trace showing the unsafe state or unauthorized path.
- A direct code path whose preconditions are established by existing tests or configuration.
Do not require a full end-to-end reproduction when a focused unit or integration test can prove the claim. Conversely, do not accept a static warning about authorization, data leakage, or race conditions when the reviewer has not established that the path is reachable. For security-sensitive work, run the reproduction without exposing production secrets or using a live customer environment.
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Failure to reproduce does not prove that a defect is impossible. It means the item has not met the blocking evidence threshold. Record the uncertainty, identify the missing precondition, and decide whether a human security owner needs to investigate. This avoids both extremes: treating every suspicion as a vulnerability and dismissing a serious concern merely because one test did not trigger it.
Deduplicate findings and rank the root cause
Anthropic documents deduplication and severity ranking as review capabilities. Use them deliberately. If five comments describe the same missing authorization check through five endpoints, report one root cause with the affected locations listed, not five independent blockers. Fixing the root cause should close the related comments.
Rank the defect by impact and exploitability, not by how many symptoms the model can enumerate. A practical triage table is:
| Finding class | Required evidence | Pull-request action |
|---|---|---|
| High severity | Reachable failure path plus a reproducible test, command, or execution trace | Fix or document an approved exception before merge |
| Medium severity | Concrete affected behavior and code-level evidence; reproduction preferred | Fix when in scope, otherwise create a tracked follow-up |
| Low severity | Specific maintainability or clarity concern without material current impact | Log for later maintenance; do not reopen the completed review |
When several comments have the same root cause, retain the clearest location and link the other affected paths in its evidence. This keeps the developer’s work queue proportional to the number of fixes actually required.
A stopping protocol you can run on every pull request
- Pass 1 — inspect the diff only. Ask for correctness, security, and test-impact review. Exclude style brainstorming and unrelated repository cleanup.
- Normalize each finding. Require location, affected behavior, severity, failure scenario, and evidence. Convert vague comments into questions for validation or discard them.
- Validate high-severity items. Run a minimal failing test, a reproducible command, or isolated execution. Preserve the command and result in the pull-request record.
- Deduplicate. Merge comments that share a root cause. Keep one owner and one remediation path for that cause.
- Resolve the blocking set. Fix each high-severity issue or explicitly dismiss it with an owner, reason, and any follow-up. Do not leave a high-severity item in an ambiguous “revisit later” state.
- Run deterministic checks. Execute the repository’s CI checks and targeted tests for the changed interfaces. A clean AI review does not replace these checks.
- Close the pass. Stop when the diff is covered, the blocking set is resolved or documented, and no new evidence appeared during the agreed validation. Put lower-severity observations in the maintenance queue without starting another review cycle.
Start a new pass only when something material changes: the diff is amended, an affected interface is added, repository guidance changes, or a new test or execution trace supplies evidence that was unavailable before.
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Turn repeated comments into engineering controls
OWASP’s guidance is direct: “No feedback loop — the same issue found repeatedly in review should become a lint rule, library helper, or training topic.” Repeated AI comments are a signal that the team is paying review-time cost for a rule that could be enforced earlier.
Choose the control that matches the failure
- Use a lint rule for a local syntax or API misuse that can be detected deterministically.
- Use a shared library helper when developers repeatedly implement the same security or data-validation pattern.
- Use a test when the rule is behavioral, such as authorization, serialization, or retry semantics.
- Use a CI gate when the check must run for every pull request or every deployment.
- Use a training topic when the issue reflects a misunderstanding that tooling cannot reliably detect.
OWASP recommends running static application security testing (SAST), software composition analysis (SCA), and secret scanning on every pull request. Treat a clean scan as the start of review rather than the end: these tools cover known patterns and dependencies, while human and AI review still need to assess business behavior and interactions among components.
Keep a human accountable for security decisions
OWASP states: “AI-assisted review tools can summarize diffs and flag patterns, but they are a supplement: a human with accountability must still approve security-relevant changes.” That approval should not be a ceremonial click. Concentrate human attention where automated pattern matching is weakest:
- Authorization decisions and privilege boundaries.
- Business logic, abuse cases, and state transitions.
- Data flows, privacy assumptions, and retention.
- Threat-model assumptions that the patch relies on.
- Whether the proposed fix changes behavior outside the visible diff.
Have the accountable reviewer inspect the evidence for each high-severity item, the rationale for every dismissal, and the interaction between the patch and existing controls. The AI can organize the investigation; it should not silently accept its own exception.
Optimize for precision, not comment count
OpenAI’s code-verification article says, “Precision is more important for usability than recall.” In a pull-request workflow, a smaller set of well-supported findings is more useful than a catalog of conceivable issues that developers must repeatedly disprove.
Repository-wide context and execution access can improve both recall and precision because the reviewer can follow real dependencies and test claims. Add that context intentionally: provide the relevant interfaces, configuration, and test commands rather than granting an unbounded mandate to inspect everything. Ask the reviewer to report only issues that meet the contract, and to state what evidence is missing when it cannot validate a suspicion.
Measure whether the process is useful with operational signals such as the proportion of findings accepted after validation, the number of duplicate comments per root cause, the time spent on dismissed findings, and the number of recurring categories converted into automated controls. Do not use raw comment volume as a quality score.
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Dismiss accepted exceptions in a way the team can audit
GitHub documents dismissing findings when they concern legacy code, known exceptions, or false positives that do not pose a real quality risk. A dismissal is safer when it records:
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- The specific finding and code location.
- The category: legacy behavior, approved exception, or false positive.
- The evidence supporting the decision.
- The responsible reviewer or team.
- A follow-up date or control when the exception is temporary.
If the same exception is dismissed repeatedly, encode it in repository guidance or the rule configuration. Otherwise each pull request will spend time re-litigating a decision the team has already made. Do not use a broad “ignore all” rule when only one path is approved; narrow exceptions preserve signal elsewhere.
How many review passes are enough?
There is no useful universal number. One pass can be enough for a small, well-tested change when it meets the contract and deterministic checks pass. A security-sensitive or cross-service change may need additional validation, but the additional pass should be tied to a specific new input: a threat-model question, a test environment, or an affected interface that was not available earlier.
Use this decision test rather than counting passes:
- Is the reviewer looking at the final diff?
- Are all affected interfaces and local rules supplied?
- Does every blocking finding have a location, scenario, severity, and evidence?
- Have high-severity claims been reproduced or otherwise established?
- Have duplicate symptoms been collapsed into root causes?
- Are CI and targeted tests green?
- Are lower-severity observations recorded without reopening the completed pass?
If the answers are yes, another identical prompt is unlikely to increase confidence. Change the evidence or the scope instead of repeating the same request.
Compare review workflows by capability, not by comment volume
When choosing an AI review or security-validation workflow, compare the controls that determine signal and accountability:
| Capability | Question to ask | Why it matters |
|---|---|---|
| Behavioral verification | Can the tool reproduce a finding with a test, command, or isolated execution? | Separates reachable defects from plausible but unproven concerns. |
| Repository context | Can it read versioned guidance and the interfaces affected by the diff? | Applies local architecture and security policy instead of generic preferences. |
| Deduplication | Does it group symptoms that share one root cause? | Prevents one defect from becoming many blocking comments. |
| Severity controls | Can the team choose which levels block and which are logged? | Creates a finite stopping rule. |
| Configurable guidance | Can approved exceptions and “do not flag” conventions be versioned? | Stops recurring false positives without hiding unrelated defects. |
| CI integration | Can it run alongside deterministic SAST, SCA, secret scanning, and tests? | Combines pattern coverage with behavioral checks. |
| Dismissal auditability | Does each dismissal retain its reason, evidence, and owner? | Makes exceptions reviewable instead of invisible. |
| Human approval points | Is an accountable person required for security-relevant changes? | Keeps authorization, business logic, and threat-model decisions under human responsibility. |
A review prompt that enforces the contract
The wording of the request should make the stopping rule executable. Adapt the following to your repository’s terminology:
Review the current pull-request diff and only the interfaces listed in the pull-request description. Apply the repository guidance for architecture, security assumptions, and accepted exceptions. Report a finding only when you can provide a precise file and line, affected behavior, severity, a concrete failure scenario, and evidence from code, tests, or execution. Validate high-severity claims with a minimal reproduction or isolated command when possible. Deduplicate comments that share a root cause. Do not report style suggestions or speculative concerns as blockers. At the end, state whether any high-severity actionable finding remains and whether the agreed CI and targeted tests pass.The prompt does not replace repository policy or human approval. It makes the expected behavior visible and gives the reviewer a clear way to say that no blocking finding remains.
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The reviewer produces more comments after every fix
Check whether the fix changed the diff or exposed a new interface. If it did, review that new evidence and update the contract. If it did not, require locations and reproductions for the new comments, deduplicate them, and close unverified observations instead of launching another unrestricted pass.
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Most comments are false positives
Add the missing architecture rule or accepted exception to repository guidance. Then require behavioral evidence and narrow the reviewer’s scope. A recurring false positive that cannot be expressed as policy may indicate that the rule belongs in configuration or should be removed from the blocking review.
One root cause creates dozens of findings
Ask for grouping by root cause and one remediation owner. After the underlying helper, authorization check, or validation rule is corrected, rerun targeted tests rather than asking for a repository-wide brainstorming pass.
A serious security concern cannot be reproduced
Do not silently dismiss it. Record the missing precondition, involve the accountable security reviewer, and decide whether an isolated environment or additional instrumentation is needed. “Not reproduced” is a validation status, not evidence that the risk is harmless.
CI is green but the AI keeps warning
CI passing establishes that the configured checks passed; it does not automatically establish that every business rule is correct. Require the AI warning to meet the same evidence threshold as any other finding. If it cannot, log it as a non-blocking observation and decide whether a new test or lint rule should cover the category.
What the published figures do—and do not—tell you
Several vendor-documented figures describe workflow scale, not a universal guarantee for your repository:
| Figure | Source and date | Qualification |
|---|---|---|
| Roughly 200 times fewer human approval interruptions | OpenAI Auto-review report, 2026 | A reported result for that Auto-review system; it is not a promise for every codebase or configuration. |
| Around 99% of reviewed actions approved by Auto-review | OpenAI Auto-review report, 2026 | A reported approval rate for that system and workflow, not an independent benchmark of AI review accuracy. |
| 20 minutes average completion time | Anthropic Code Review documentation, 2026 | Product documentation, not an independent benchmark; actual time depends on repository size, checks, and required validation. |
Use these numbers as context for automation discussions, not as substitutes for measuring your own accepted-finding rate, validation time, and escaped defects.
The practical definition of “done”
An AI review is complete when the final diff and affected interfaces have been examined under the repository’s rules, every blocking finding is fixed or explicitly accepted, high-severity claims have evidence, duplicate symptoms are collapsed, deterministic checks pass, and the accountable human approves security-relevant behavior. Anything below that bar belongs in a tracked maintenance queue or a future engineering control—not in an endless loop of identical review prompts.
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