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This is a risk-based checklist, not a guarantee or a universal standard. A one-line documentation fix may need only a narrow test and diff review; an authentication, payment, data-migration, or production-infrastructure change deserves repository context, focused validation, and a human reviewer.
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Start with the request, not the generated diff
AI agents can produce a polished pull request before they have demonstrated that they understood the task. I begin by restating the requested outcome in concrete terms: what behavior must change, what must remain unchanged, which users or systems are affected, and what evidence would show success. Then I compare that statement with the actual files and behavior in the diff.
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GitHub’s official guidance for reviewing code generated by Copilot, ChatGPT, and other agents recommends checking requirements, conventions, compilation, tests, warnings, security, dependencies, and code quality. The guidance is written in a vendor-specific documentation context, but these review questions apply to any coding agent. See GitHub’s “Review AI-generated code” guide for its source checklist.
#1 Best Overall
The seven checks below are my practical synthesis. I do not treat a clean automated review as proof that a change is correct. As OpenAI’s code-verification article puts it, We cannot assume that code-generating systems are trustworthy or correct; we must check their work.
(OpenAI, December 1, 2025).
My seven checks at a glance
| Check | Question | Evidence I want |
|---|---|---|
| 1. Requested outcome | Does the diff solve the stated problem without silently changing the goal? | Requirements mapped to changed behavior and files |
| 2. Architecture | Does it fit surrounding interfaces, patterns, data flow, and dependencies? | Relevant call sites, implementations, configuration, and conventions reviewed |
| 3. Behavior verification | Can the important behavior be demonstrated, including failure and edge cases? | Compilation, tests, warnings, static checks, and focused manual or integration evidence |
| 4. Evidence integrity | Did the patch weaken the tests or configuration that would expose a defect? | No unexplained deleted, skipped, diluted, or bypassed checks |
| 5. Security | Are inputs, permissions, state transitions, and dangerous operations handled safely? | Threat-focused review and validation of plausible high-impact issues |
| 6. APIs and dependencies | Are every new API and package real, maintained, permitted, and appropriate? | Documentation, lockfile, provenance, maintenance, and license checks |
| 7. Maintainability and review | Can people understand, operate, and safely change the result later? | Readable code, useful comments, repeatable CI, and human review when warranted |
1. Does the diff solve the requested problem?
Translate the request into observable outcomes
Write down the acceptance conditions before reading implementation details. “Add rate limiting” is not a complete condition; the real requirements might include which endpoint is limited, how unauthenticated callers are identified, what response is returned, whether limits differ by account tier, and how counters expire. An agent can implement a plausible limiter while missing any of those business rules.
For each requirement, identify the changed behavior, the user or system that observes it, and a way to verify it. Mark assumptions that the request did not settle. If the agent chose a default, such as treating a missing field as empty text, decide whether that default is actually allowed rather than accepting it because the code looks tidy.
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Look for scope drift and omissions
- List every file changed and explain why it belongs in the task.
- Flag unrelated formatting, renaming, generated-file churn, or configuration edits that make review harder.
- Check that every requested path is covered, including error handling, migration steps, documentation, and backward compatibility where applicable.
- Compare the implementation with stated business rules instead of inferring correctness from familiar names.
A useful review comment is specific: “The request says limits are per account, but this key uses source IP, so two users behind one gateway share a bucket.” That is more actionable than “This may not work.”
Test the intent, not just the happy path
Ask what a reasonable user would do that the prompt did not spell out. Boundary values, repeated requests, retries, missing permissions, old records, concurrent updates, and partial failures often expose an intent mismatch. GitHub’s guidance captures this risk directly: Be skeptical of code that ‘looks right’ but doesn’t match your intent.
Treat that as a prompt to inspect assumptions, not as a claim that every generated change is wrong.
2. Does it fit the surrounding architecture?
Read beyond the changed lines
A diff is a useful index, not the whole system. Read the interface the change implements, neighboring implementations, callers, configuration, data models, error conventions, and the tests that exercise similar behavior. Search for the symbol being changed and for equivalent features elsewhere in the repository. This reveals contracts that may not be written in the task description.
For example, a new service method may compile while violating the repository’s transaction boundary. A handler may return the right status code but bypass the shared authorization middleware. A database query may work in isolation while ignoring the project’s pagination or tenant-filtering convention.
Check interfaces and dependency direction
- Confirm that the implementation uses the existing abstraction rather than introducing a parallel one without a reason.
- Check whether imports preserve the project’s dependency direction; a low-level package should not suddenly depend on an application layer.
- Verify serialization formats, nullability, time zones, retry behavior, and error types against established code.
- Inspect configuration defaults and deployment manifests when the change adds a setting or external service.
OpenAI’s verification guidance explains why execution and repository access matter: they expose context and dependency interactions that a diff-only review can miss. The article also warns that its evaluation has limits, including a test set of issues already identified by humans. That is a reason to combine repository context with judgment, not to replace either one.
Identify integration seams
Pay special attention to boundaries: API handlers to services, services to databases, queues to workers, and code to third-party systems. At each seam, ask what happens on timeout, duplicate delivery, malformed data, version skew, and partial success. AI-generated code often handles the local function correctly while making an invalid assumption about the component on the other side.
Rank #2
3. Can the behavior be verified?
Use layered evidence
Start with the repository’s documented build and test commands, then add the narrowest checks that exercise the changed behavior. Compilation catches type and interface failures; unit tests check local logic; integration tests expose real dependency interactions; static analysis and linting catch repeatable quality and security patterns; a focused manual or end-to-end check can confirm user-visible behavior.
Record what each check actually covers. A green unit-test suite is evidence about those test cases, not proof that untested inputs, production configuration, concurrency, or operational failure modes are correct. If a test cannot run in the current environment, say so and identify the residual risk instead of treating it as a pass.
Look for missing edge cases
- Empty, null, malformed, oversized, and boundary-value inputs
- Unauthorized, unauthenticated, and partially authorized callers
- Retries, duplicate events, concurrent updates, and out-of-order messages
- Timeouts, unavailable dependencies, rolled-back transactions, and partial writes
- Existing data created by older versions of the software
For a bug fix, require a regression test that would fail on the old code and pass on the new code. For a refactor, verify that externally visible behavior remains stable. For a migration, test both the transition and the rollback or recovery path that the operation requires.
Interpret warnings and coverage honestly
Do not dismiss compiler warnings, linter findings, flaky tests, or coverage drops merely because the final status is green. Determine whether a warning is pre-existing, introduced by the patch, or suppressed by a new configuration change. Coverage percentage is a signal about executed lines, not a measurement of requirement coverage or security.
4. Did the change weaken its own evidence?
Inspect tests and configuration as production code
Agents sometimes “fix” a failing test by changing the test, reducing an assertion, broadening a mock, increasing a timeout, or disabling a check. Review test and configuration hunks with the same skepticism as application code. A deleted test may represent a requirement that the implementation no longer satisfies.
- Search for removed tests, skipped cases, changed expected values, and assertions replaced with weaker existence checks.
- Check for new ignore patterns, disabled lint or type rules, relaxed security settings, and coverage exclusions.
- Review mocks and fixtures for realism: a mock that always succeeds can hide authentication, timeout, or data-shape failures.
- Compare test counts and important scenario names before and after the patch.
GitHub specifically advises questioning tests that are removed or skipped instead of fixed. Ask the author to repair the production behavior or document a deliberate, reviewed change in requirements. Never accept a green pipeline whose evidence was quietly diluted by the same change it is supposed to validate.
Detect false confidence from overfitting
An agent may tune code to visible tests while missing the general rule. Add a case that varies the input, ordering, identity, or failure mode without revealing the expected implementation. Property-based, contract, or integration checks can be valuable when a small set of examples would be easy to overfit.
5. Are security-sensitive paths handled safely?
Map the security impact of this patch
Not every change needs a formal threat model, but every change should be screened for security relevance. Identify new or modified inputs, authentication and authorization decisions, secrets, file and process operations, network calls, deserialization, database queries, tenant boundaries, and state transitions such as payment, deletion, or privilege changes.
Prioritize plausible, high-impact issues first. A new permission check on an administrative endpoint matters more than a low-severity information leak in a debug message, although both may deserve attention. OpenAI’s Codex Security validation guidance emphasizes validating security findings with focused evidence; an unverified candidate should not be reported as safe or as a confirmed vulnerability.
Rank #3
Check common failure modes
- Authorization is performed on a client-supplied identifier instead of the resource actually loaded.
- Input is validated for format but not for ownership, scope, size, or permitted values.
- Escaping is applied for one output context but the value is reused in another, such as a shell command, SQL statement, template, or URL.
- Secrets or personal data are written to logs, error responses, fixtures, or telemetry.
- State changes are not idempotent, allowing retries to duplicate an operation.
- Failure handling falls back to an insecure default, such as allowing access when a policy service is unavailable.
Validate, then document residual risk
Use a focused exploit or negative test when a security concern is plausible. Verify the authorization boundary with multiple identities, exercise malformed input, and inspect logs and responses for leakage. If the environment prevents validation, record the limitation and require an appropriate security review rather than declaring the path safe.
6. Are new APIs and dependencies real and acceptable?
Verify that every symbol exists
AI agents can invent convincing method names, package coordinates, configuration keys, and version numbers. Confirm each new API in the authoritative library documentation or the repository’s installed version. Check the actual exported symbols, supported runtime, and behavior of the version used by the project; do not rely on a code sample aimed at a different release.
For dependencies, inspect the manifest and lockfile together. Confirm that the package resolves from the intended registry, that the checksum or integrity data is present, and that the transitive dependency graph is understood. A package with a similar name can be malicious or simply unrelated to the intended project.
Assess provenance, maintenance, and licensing
- Identify the actual publisher and repository, not just the package name.
- Check release activity, issue responsiveness, supported runtimes, and known vulnerability notices.
- Confirm that the license is compatible with the project’s distribution and obligations.
- Look for unnecessary new packages when the existing standard library or repository utility already provides the capability.
- Review update and removal plans so an abandoned dependency does not become permanent infrastructure.
GitHub’s generated-code guidance calls out hallucinated or suspicious packages and recommends checking origins and licenses. Treat a dependency as a supply-chain decision, not a convenience import.
7. Is the result maintainable and appropriately reviewed?
Review the code a future maintainer will see
Read the final code without the agent’s explanation. Names should describe domain concepts, control flow should be easy to follow, and comments should explain constraints or decisions rather than restate syntax. Remove speculative abstractions, duplicated branches, dead compatibility code, and generated prose that obscures the real behavior.
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Make repeatable checks part of CI
Use continuous integration for the checks that can be automated consistently: formatting, lint, type checking, compilation, unit and integration tests, dependency and secret scanning, security analysis, and coverage or mutation thresholds where the project uses them. Keep the configuration reviewable so an agent cannot silently bypass a gate by editing its settings.
Bring in a human when the risk warrants it
Ask a teammate to review changes that are security-sensitive, architecturally novel, difficult to test, broad in scope, or hard to roll back. The second reviewer should receive the request, relevant design context, test evidence, and known limitations—not just a green status page. Human review is also useful when the agent had broad tool access or when the patch changes policy or business rules that are not encoded in tests.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Automated checks and human judgment solve different problems
| Review dimension | Automated checks are strong at | Human review is still needed for |
|---|---|---|
| Evidence quality | Running repeatable builds, tests, linters, scanners, and policy gates | Deciding whether the evidence is relevant to the changed behavior and whether important cases are missing |
| Repository context | Searching symbols, comparing patterns, and enforcing machine-readable conventions | Understanding unwritten architecture, business assumptions, and trade-offs across components |
| Security impact | Detecting known patterns, vulnerable versions, and policy violations | Prioritizing plausible high-impact abuse paths and validating whether a finding is real |
| Judgment | Applying explicit, repeatable rules | Assessing intent, clarity, reversibility, and whether a design is appropriate for this system |
| Repeatability | Producing consistent results on every change | Adapting scrutiny to unusual, novel, or high-consequence changes |
The right workflow combines both. Automate what should be checked identically every time, then reserve human attention for intent, architecture, uncertainty, and sensitive trade-offs.
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Low-risk changes
For isolated documentation, wording, or narrowly scoped internal refactors, verify the diff, run the affected checks, and confirm that no configuration or dependency changed. A full second review may not be necessary if repository policy allows a lightweight path.
Medium-risk changes
For user-visible behavior, data handling, or shared libraries, read neighboring implementations, add regression and edge-case tests, inspect dependency changes, and obtain a reviewer familiar with the component.
High-risk changes
For authentication, authorization, payments, secrets, destructive operations, migrations, production infrastructure, or broad cross-service changes, require focused security validation, integration evidence, rollback planning, and at least one independent human review. Restrict agent permissions and make the execution record available for audit.
Operational controls matter when an agent can execute code
Reviewing the final diff is only part of the control surface. If an agent can run commands, access repositories, modify files, or call external services, define a sandbox boundary, require approval for sensitive operations, limit credentials, and retain agent-aware logs. OpenAI describes these controls in “Running Codex safely at OpenAI” (May 8, 2026). The exact implementation depends on the platform, but the principle is general: constrain what the agent can do, make consequential actions visible, and preserve enough history to investigate them.
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What AI-review deployment numbers do—and do not—prove
OpenAI reported that 36% of pull requests entirely generated by its Codex cloud received a code-review comment, and 46% of those comments led the author to make a code change. It also reported that 52.7% of cases in which a reviewer left a comment resulted in an author code change, more than 100,000 external pull requests per day were handled by the system as of October 2025, and more than 80% of comment reactions were positive during the early deployment signals described in its December 1, 2025 article.
Those are company-reported figures about one system and deployment, not independent estimates of AI-generated code quality or evidence that another agent will perform similarly. The article notes evaluation limitations, including a test set of issues already identified by humans. The practical lesson is narrower: automated review can surface useful issues at scale, but its results still require relevance checks and human judgment. As the same article states, Over reliance is a serious risk.
A compact review record I keep for each change
- Intent: copy the requested outcome and list any assumptions or unanswered business rules.
- Scope: explain every changed file and flag unrelated edits.
- Context: note the interfaces, call sites, configurations, and analogous implementations inspected.
- Evidence: record the build, tests, static checks, manual checks, and untested scenarios.
- Evidence integrity: record whether tests, assertions, mocks, or policy settings were removed or weakened.
- Security: identify affected boundaries, validations performed, findings, and residual risk.
- Dependencies: verify APIs, versions, provenance, maintenance, and license compatibility.
- Disposition: approve, request changes, or escalate to a specialist reviewer with the reason.
This record keeps the review proportional while making the decision auditable. It also gives the next maintainer a concise explanation of what was checked and what remains uncertain.
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