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Start by defining what the agent was supposed to change
A review cannot establish correctness if the requirement is vague. Ask the agent or author to provide a short change record before you judge the implementation. It should state:
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- The user or business requirement being addressed.
- The files and services changed, including generated files and configuration.
- Assumptions about inputs, existing behavior, data formats, concurrency, and failure conditions.
- New or upgraded dependencies, permissions, network calls, migrations, feature flags, and operational requirements.
- Potential data, privacy, and security impact.
- Tests and tools already run, with their exact results.
Compare that statement with the issue, design notes, API contracts, repository conventions, and compatibility promises. If the agent cannot explain why a file changed, treat that as an unresolved review item rather than a harmless mystery.
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Separate requested behavior from implementation detail
Write down observable outcomes before reading the code in depth. For an API, this may include accepted inputs, response shape, status codes, authorization rules, rate limits, and behavior when a downstream service is unavailable. For a user interface, include loading, empty, error, keyboard, accessibility, and localization states. For a data change, define migration order, backward compatibility, rollback implications, and whether old clients can continue operating.
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This prevents a polished implementation from redefining the requirement. An AI agent can produce code that compiles and looks idiomatic while silently dropping a constraint that appeared only in a ticket comment or an existing convention.
Inspect the complete change, not only the new function
Read the full diff from the known-good revision. Include renamed and deleted files, lockfiles, generated artifacts, build scripts, infrastructure, database migrations, permissions, and documentation that changes an operational procedure. Then trace every interface the change touches.
- Follow data from the public input boundary to storage, logs, queues, and external services.
- Check callers and consumers for assumptions about types, ordering, nullability, errors, time zones, and retries.
- Look for changes to serialization, caching, transactions, idempotency, and concurrency.
- Check whether a new package is necessary, maintained, correctly licensed, and pulled from the expected registry.
- Compare configuration defaults across development, test, staging, and production.
Pay special attention to code that appears unrelated to the request. Unnecessary refactors increase the blast radius and make a later rollback harder. A deleted test, disabled validation, broadened permission, or skipped error branch can be more significant than the feature described in the pull request.
Check for plausible-looking hallucinations
Verify every external API, SDK method, configuration key, command-line option, and framework hook against the authoritative documentation for the version in use. AI agents may invent a method name, combine two similar libraries, use an obsolete parameter, or assume behavior from a different language binding. Compilation catches some of these errors; integration checks and documentation review catch the rest.
Also compare the code with local conventions. A repository may require a particular transaction wrapper, logging library, error type, feature-flag mechanism, or dependency-injection pattern. Code that is valid in isolation can violate those conventions and fail only under production load or during an operational task.
Run mechanical checks before subjective review
Start with deterministic tools. GitHub’s review guidance recommends running automated tests and static analysis first, checking compilation and warnings, and using security, dependency, quality, and coverage tools. The result should be recorded with the commit or pull request so another reviewer can reproduce it.
Compile, type-check, lint, and format
- Build or compile the exact revision intended for review. Treat warnings as review findings when they indicate unsafe conversions, unreachable code, deprecated APIs, or ignored errors.
- Run the repository’s type checker with production settings. Confirm that generated code and optional platforms are included where they will run.
- Run linting and formatting checks without allowing the tool to silently rewrite the branch. A clean formatter result should be visible in CI.
- Run static analysis and quality rules appropriate to the language, framework, and risk profile.
Do not infer success from a local editor showing no errors. Verify the same commands and tool versions used by continuous integration, and inspect the logs for skipped modules, excluded paths, or network failures that made a check incomplete.
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Exercise the test pyramid
- Unit tests: test pure logic, boundary values, error mapping, and state transitions quickly and repeatedly.
- Integration tests: verify real database, queue, filesystem, identity, and service contracts where mocks could conceal incompatibilities.
- End-to-end tests: cover the critical user or business journey through the deployed interfaces, including authentication and failure recovery.
Run the tests that match the changed surface, not only the smallest test file adjacent to the edit. A change to a shared parser, authorization middleware, schema, or client library deserves downstream integration and end-to-end coverage.
Add security and supply-chain checks
Run dependency vulnerability and license checks, secret scanning, static application security testing, and relevant container or infrastructure scans. Confirm that lockfiles are consistent and that no credentials, tokens, private endpoints, debug flags, or test fixtures containing sensitive data entered the repository.
NIST’s DevSecOps guidance recommends threat modeling, automated tests, static scanning, secret detection, black-box and structural tests, historical tests, fuzzing, and web-application scanners where applicable. Select the checks that fit the system, but document exclusions and their reason. A scanner that was not run is not evidence that the code passed it.
Review behavior and architecture deliberately
Once mechanical checks are complete, review whether the implementation actually solves the requested problem and remains safe in its environment.
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Trace at least one successful case, the smallest and largest valid inputs, missing and malformed inputs, duplicate requests, timeouts, partial failures, retries, cancellations, and unexpected downstream responses. Check that errors are returned at the correct abstraction level, logged without secrets, and translated into actionable messages for operators or users.
Examine resource behavior as well: memory growth, file descriptors, database connections, queue depth, thread or async-task limits, and cleanup after cancellation. AI-generated code often handles the happy path while leaving a leaked resource or unbounded retry loop in an exception path.
Verify compatibility and rollout assumptions
- Can old clients call the new server, and can the new client talk to an old server during a rolling deployment?
- Are schema changes additive first, with destructive work delayed until all readers and writers are upgraded?
- Does a feature flag actually disable all new side effects, or only hide the user interface?
- Are caches, queues, indexes, and background jobs consistent when only part of the fleet has the change?
- Is there a documented migration and reversal plan for persistent data?
Review architecture for unnecessary coupling and privilege. A small feature should not introduce a broad administrative permission, a new network trust relationship, or a package that executes installation scripts without a clear need.
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Threat-model the generated change
Map new trust boundaries and data flows. Identify who can supply input, who can invoke the changed operation, where data is stored, and which identities or services are trusted. Then test the controls rather than accepting their names at face value.
Authentication, authorization, and input handling
- Confirm authentication is required on every relevant route, job, command, and administrative path.
- Check authorization at the object and action level, including tenant or account separation. Do not rely on a hidden button or client-side check.
- Validate type, length, encoding, range, and allowed values at the boundary. Re-validate after deserialization or protocol translation where the representation changes.
- Check protection against injection, path traversal, request forgery, unsafe redirects, deserialization attacks, and denial-of-service inputs relevant to the stack.
Secrets, cryptography, logging, and privacy
Ensure secrets come from the approved secret store and are never embedded in source, prompts, fixtures, logs, error messages, or telemetry. Verify cryptographic algorithms, key handling, nonce or IV usage, certificate validation, and password-processing choices against the platform’s approved approach. Confirm that logs contain enough context for diagnosis but redact tokens, personal data, and sensitive payloads.
Document data retention and cross-border or third-party transfers introduced by the change. An AI-generated integration may send more fields than the feature requires, or include user content in a diagnostic request that was not part of the original design.
Dependencies and provenance
Inspect the package name, version, source registry, integrity metadata, transitive dependencies, and license. Confirm that the dependency is maintained and that its permissions and runtime behavior fit the application. A familiar package name is not proof that the selected artifact is the intended one.
Do not treat AI-generated tests as proof
Generated tests are claims about behavior, written by the same kind of system that produced the implementation. Read them as carefully as production code.
Look for meaningful assertions
- Each test should fail when the behavior it names is broken; remove tests that only execute lines or assert that a value is not null.
- Assertions should verify outputs, side effects, security decisions, and error details that matter to the contract.
- Inputs should include boundaries, malformed values, empty collections, duplicate events, permission differences, and dependency failures where relevant.
- Tests should avoid overfitting to private method names, incidental call order, or the exact implementation when the contract allows alternatives.
- Mocks should not replace every meaningful dependency. Include contract or integration tests for serialization, persistence, identity, and external protocols.
Add missing tests yourself and run mutation, fuzz, or property-based checks when the risk justifies them. Mutation testing is useful when a test suite reports high coverage but may not detect changed operators, removed branches, or weakened conditions. Fuzzing is valuable for parsers, protocol handlers, file formats, and security-sensitive boundaries.
NIST’s 2025 GenAI (Pilot) Code Challenge Evaluation Plan evaluates the effectiveness of AI-generated unit tests for elementary Python code. It is an evaluation plan, not a universal reliability statistic; passing generated tests alone is therefore not evidence that production code is correct.
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Keep approval and deployment gates human-controlled
Use protected branches, required reviewers, required status checks, and deployment approvals. Assign ownership to people who understand the affected system and can accept or reject its risk. The author of an agent prompt should not be the only approver for a sensitive change.
NIST’s DevSecOps reference model states that AI-generated corrective actions should be treated as proposed inputs to future planning or development and should not modify software, configuration, or system state without review and approval through established processes. In practice, an agent may open a pull request, propose a patch, or prepare a deployment, but it should not unilaterally merge, change production configuration, rotate credentials, or redeploy a fix.
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An AI reviewer can summarize a diff, identify repeated patterns, suggest missing cases, and flag suspicious changes. Give it the requirement, relevant conventions, and the exact diff, then verify every finding. It can miss a domain-specific invariant or confidently endorse an incorrect assumption.
OpenAI’s guidance describes Codex Code Review as an additional reviewer; tests, branch protections, and required approvals remain hard enforcement. Keep mechanical checks in CI even when an AI review bot comments on every pull request. Do not let a green AI review status substitute for an accountable human decision.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Prepare the rollback before merging
A rollback plan is part of readiness, not an emergency improvisation. Identify the exact commit or artifact that will be deployed, the last known-good revision, the owner authorized to reverse it, and the health signals that will determine success.
Unmerged or local work
- Stop the agent and prevent additional automation from changing the branch.
- Preserve useful work on a separate branch or patch if it may be needed for diagnosis.
- Confirm the target known-good commit with
git log, the pull request history, or the release record. - Check for uncommitted work and decide explicitly whether it must be saved before using the team’s normal reset or checkout procedure.
- Restore the known-good revision, rerun the relevant checks, and record what was discarded and what was retained.
Never run a destructive reset merely because an agent says the branch is clean. Verify the commit and working-tree state yourself.
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Prefer a new revert commit or revert pull request. GitHub documents that reverting a merged pull request creates a new pull request that reverses the original merge. If the merge contains conflicts or several independent commits, individual commits may need to be reverted in a controlled order.
The usual Git operation, after confirming the commit and reviewing the resulting diff, is:
git revert <commit>git revert records an inverse commit while preserving existing history. That is safer for a shared branch than rewriting history with a reset and force-push, because other developers, builds, and deployment records continue to reference the same commits.
Incidents during deployment
- Stop further rollout and freeze unrelated deployments so the signal is attributable.
- Identify the deployed commit, image, package, migration level, and feature-flag state.
- Redeploy the last known-good revision or use the platform’s tested rollback mechanism.
- Run health checks, smoke tests, key user journeys, and data-integrity checks. Confirm that queues, caches, background workers, and scheduled jobs are healthy.
- Determine whether a database or other persistent-data migration means a binary rollback is unsafe. If so, stabilize the service and prepare a forward fix under the normal approval process.
- Record whether service was restored, how long recovery took, and which follow-up test or guardrail will prevent recurrence.
A rollback can restore availability while leaving bad data behind. Check duplicate writes, partial transactions, corrupted files, unauthorized changes, and messages emitted to downstream systems. Reversing application code does not automatically reverse every side effect.
After recovery
Preserve logs, metrics, traces, the original diff, review comments, test output, deployment identifiers, and the rollback decision. Open a corrective change through the same review, security, testing, and approval gates. Do not let an agent directly redeploy a fix because it authored the original change or proposed the rollback.
Choose tools and process by risk, not novelty
When comparing a hosted repository, review bot, CI service, scanner, or deployment platform, evaluate the controls around the agent rather than its generation feature alone.
| Comparison axis | Questions to answer |
|---|---|
| Review depth and ownership | Who reviews the requirement, architecture, security impact, and final diff? Are system owners required? |
| Deterministic verification | Can compilation, unit, integration, end-to-end, lint, static analysis, and coverage checks run on every proposed change? |
| Security and dependencies | Are secret, vulnerability, license, container, infrastructure, and provenance checks available and enforced? |
| Branch and approval enforcement | Can unreviewed or failing changes be blocked from merge and deployment, including changes made by automation? |
| Observability | Are deployment IDs, logs, metrics, traces, health checks, and feature-flag states tied to a revision? |
| Rollback speed and history | Can the last known-good artifact be restored quickly without rewriting shared history, and are migration constraints visible? |
| Staged rollout | Does the platform support canaries, progressive delivery, feature flags, and automatic halt conditions? |
| Auditability | Are prompts, agent outputs, approvals, test results, deployment actions, and reversions retained? |
| Technical fit | Does it support the repository’s language, build system, release platform, compliance needs, and operating model? |
A sophisticated agent with weak branch protection is less safe than a modest agent operating inside strong deterministic gates. Select the smallest automation authority that can do the job, and make every higher-impact action require an explicit human decision.
A practical pre-merge checklist
- The requirement, scope, assumptions, dependencies, data impact, and tests run are documented.
- The full diff, lockfiles, generated files, configuration, migrations, and deleted tests have been inspected.
- Compilation or type-checking, linting, formatting, static analysis, and relevant test suites pass on the intended revision.
- Security, secret, dependency, license, and supply-chain checks have run, with exclusions documented.
- Normal, boundary, concurrency, retry, timeout, authorization, and failure paths have been exercised.
- External APIs and dependencies match authoritative documentation and approved repository conventions.
- Generated tests contain meaningful assertions, and missing cases have been added independently.
- At least one accountable human has approved the change through a protected workflow.
- The deployment artifact, last known-good revision, health signals, owner, and rollback procedure are known before release.
The operating principle
AI changes do not need a special standard that is lower than normal engineering; they need normal engineering discipline applied with greater skepticism about intent and omissions. Establish behavior first, make machines perform repeatable checks, make humans own risk and approval, and preserve a recovery path that has been considered before the change reaches users.
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