The Tool Desk
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Start with scope, not syntax
Before reading individual lines, establish what the change is supposed to do and what it must not do. Read the issue, acceptance criteria, design notes, and pull-request description. Then inspect the complete diff rather than only the files highlighted by an AI assistant.
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- List every modified, added, deleted, renamed, and generated file.
- Identify new or changed dependencies and lockfile entries.
- Check whether tests were added, changed, weakened, or removed.
- Look for changes to prompts, agent instructions, hooks, or other files that influence future code generation.
- Inspect CI workflows, build scripts, deployment manifests, permissions, and infrastructure configuration.
- Confirm the change’s intended users, inputs, outputs, data stores, and failure behavior.
A small-looking AI patch can have a large blast radius if it changes a shared library, authentication middleware, a deployment workflow, or an instruction file used by later agents. Scope review tells you where to spend the most attention before implementation details create false confidence.
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Read the change as a behavior path
Trace each important path from an external input to validation, authorization, state changes, side effects, and the final response. Do not assume that a function is safe because its name sounds protective or because a scanner reports no finding.
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Map inputs and trust boundaries
For every input, identify its origin and the point at which it becomes trusted. Check request parameters, headers, uploaded files, message-queue payloads, environment variables, configuration files, and data returned by other services. Verify that the code validates type, length, encoding, range, and allowed values before use. Normalization must happen before comparisons or authorization decisions; otherwise equivalent representations can bypass a check.
Verify authorization at each boundary
Authentication establishes who a caller is; authorization determines what that caller may do. Follow the identifier for the user, tenant, account, document, or job through every database query and service call. Confirm that ownership or role checks occur on the server side and cannot be skipped by calling a lower-level endpoint directly. Watch for object references taken from the request and used without checking that the object belongs to the authenticated principal.
Check invariants and business rules
Write down the rules that must always remain true, then test whether the patch preserves them. Examples include a balance never becoming negative, an order not shipping twice, a disabled account not receiving a new session, or a tenant never seeing another tenant’s records. AI-generated code often implements the visible happy path while omitting a rule that was implicit in surrounding code.
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Inspect failure and recovery paths
Follow what happens when a dependency times out, a record is missing, a credential is expired, a transaction partially completes, or a downstream response is malformed. Confirm that errors do not leak secrets or internal details, that retries cannot duplicate side effects, and that partial work is rolled back or made safely repeatable. Check whether errors are logged with enough context to investigate without recording passwords, tokens, or personal data.
Consider state and concurrency
Look for time-of-check/time-of-use gaps, lost updates, duplicate jobs, and assumptions that a value cannot change between two operations. Review locking, transaction boundaries, idempotency keys, queue semantics, and cache invalidation. Add a concurrency test when two requests can update the same resource or when a job may be retried.
Review dependencies instead of trusting generated names
For every added or upgraded package, verify that it is the intended project and comes from the expected registry and maintainer. AI systems can suggest a plausible package name that is misspelled, abandoned, malicious, or unrelated to the capability you need.
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- Confirm the package exists in the registry used by the project.
- Check the repository, documentation, license, release history, and maintenance activity.
- Compare the package name and import path with the project’s established conventions.
- Review the lockfile diff and ensure the resolved version is the one you intended.
- Run the project’s software-composition and dependency vulnerability checks.
- Check transitive dependencies and install or build scripts for unexpected network access or command execution.
A dependency review is also needed when the generated code changes a version without changing source imports. A lockfile-only update can alter production behavior, licensing, or the attack surface.
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Treat generated tests as untrusted code
Tests written by the same generation process can encode the implementation’s assumptions rather than the requirement. A green test suite is meaningful only when the tests exercise the behavior that users and operators actually need.
Look for tests that lost power
- Deleted tests or reduced case counts.
- Assertions changed from exact values to broad truthiness checks.
- Exceptions swallowed so a failing operation appears successful.
- Mocks that replace the very authorization, parsing, persistence, or network behavior under review.
- Fixtures that omit malformed, empty, expired, duplicate, or cross-tenant data.
- Snapshots updated without an independent explanation of every changed line.
Design independent challenge cases
Derive tests from the requirement and threat model, not from the generated implementation. Include invalid types, boundary values, missing fields, duplicate requests, expired credentials, revoked permissions, unexpected encodings, partial downstream failures, and simultaneous updates where applicable. For security-critical behavior, have a reviewer who did not write the generation prompt design at least some of the negative cases.
Property-based testing can check broad invariants such as “a user can read only records in the user’s tenant” across many generated inputs. Differential testing can compare a changed implementation with a trusted previous implementation or a reference model. These techniques complement ordinary examples; they do not replace a human’s judgment about whether the property itself is correct.
Run every normal automated gate
AI-written code should pass the same required checks as human-written code. Keep the project’s test, lint, type-checking, static-analysis, dependency, and secret-scanning gates enabled for every pull request.
| Layer | What it is good at | What it cannot establish alone |
|---|---|---|
| Unit and integration tests | Expected behavior and regressions covered by their cases | Unspecified requirements or missing negative cases |
| Linting and type checks | Syntax, style, and many interface mistakes | Correct authorization or business decisions |
| Static analysis | Known insecure patterns and suspicious data flows | Contextual flaws and acceptable-use policy |
| Dependency analysis | Known vulnerabilities and dependency provenance signals | Whether a package is appropriate for the design |
| Secret scanning | Credentials and token-like material committed to code | Secrets exposed through logs, responses, or misuse |
| Human review | Requirements, trust boundaries, and business logic | Exhaustive coverage of every execution path |
Pattern-based tools such as Semgrep can enforce project-specific rules. GitHub Code Security and CodeQL can annotate pull requests with code-scanning findings. Snyk Code can provide static-analysis feedback in pull requests and development environments. These are useful layers, not substitutes for understanding the change. A clean report means that the configured checks found nothing; it does not mean the behavior is correct.
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Raise the bar for sensitive changes
Use a stronger approval path when a patch touches security-sensitive code or a control plane. Require an appropriate code owner, trained reviewer, or security reviewer, and apply the organization’s severity policy to block critical findings.
| Change area | Additional review questions |
|---|---|
| Authentication and authorization | Can any caller bypass the check, reuse a token, or access another principal’s data? |
| Cryptography and key handling | Are algorithms, modes, key storage, rotation, and error behavior consistent with policy? |
| Parsing and deserialization | Are malformed, oversized, recursive, and unexpected inputs rejected safely? |
| File uploads and processing | Are type checks, size limits, storage paths, execution risks, and isolation enforced? |
| Sensitive data access | Is collection, use, logging, retention, and deletion limited to the stated purpose? |
| Public endpoints and integrations | Are rate limits, authentication, validation, timeouts, retries, and third-party trust handled? |
| IAM, CI/CD, and deployment policy | Did permissions, network reachability, secrets, or production controls become broader? |
Apply the same elevated threshold to changes that can influence later AI output. Agent instruction files, repository prompts, hooks, and policy configuration are executable governance: a malicious or careless edit can steer future generations toward unsafe behavior.
Audit CI/CD and build machinery
Review workflow files with the same care as application code. Check whether untrusted pull-request code runs with privileged tokens, whether a job gained write access, and whether a new action or script can reach production secrets or internal networks.
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- Limit each job’s permissions to the minimum it needs.
- Separate untrusted build steps from deployment credentials.
- Check that logs do not print tokens, connection strings, or personal data.
- Verify that encryption, signing, artifact checks, and audit logging were not disabled.
- Confirm that deployment, sandbox, and approval rules still require the intended human gates.
An AI-generated workflow can be syntactically valid and still create a serious supply-chain or privilege-escalation path. Review the effective permissions and execution context, not only the YAML formatting.
Use a risk-based approval model
Not every patch needs the same number of reviewers, but every patch needs an accountable owner. A practical model combines sensitivity, change size, dependency impact, and reversibility.
| Risk tier | Typical examples | Minimum decision standard |
|---|---|---|
| Routine | Documentation, isolated presentation text, or a low-impact refactor with no behavior change | Diff review, normal automated gates, and one reviewer who understands the change |
| Elevated | New dependency, persistence change, external integration, or nontrivial business logic | Independent negative tests, dependency verification, full automated checks, and an owner familiar with the subsystem |
| Critical | Authentication, authorization, cryptography, sensitive data, public attack surface, IAM, CI/CD, deployment, or sandbox policy | Code-owner or security review, explicit threat and failure-path analysis, required checks, and documented approval |
Change size is not a reliable proxy for risk. A two-line permission change can matter more than a large refactor. Conversely, generated files may be large but low risk if their source and regeneration process are controlled. Review the effect, not the line count.
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Require explainability and ownership
The approving person must be able to explain what the change does, why it satisfies the requirement, what assumptions it makes, and how important behaviors are tested. If the reviewer cannot describe the path from input to side effect, the change is not ready to merge.
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Do not delegate accountability to an AI review tool. An automated reviewer can suggest files or findings, but a human must decide whether the evidence is sufficient and accept responsibility for the approval.
A repeatable pull-request procedure
- Define the contract. Record the intended behavior, forbidden behavior, affected users, data, and operational constraints.
- Inventory the diff. Include source, tests, generated artifacts, dependencies, prompts, workflows, infrastructure, and permissions.
- Classify risk. Mark sensitive paths and decide whether code-owner or security review is required.
- Trace execution. Follow inputs through validation, authorization, state transitions, external calls, output encoding, and error handling.
- Challenge assumptions. Add or inspect tests for malformed input, boundaries, expiry, revocation, duplication, concurrency, and partial failure.
- Verify dependencies. Confirm package identity, registry, maintainer, version, lockfile resolution, and transitive behavior.
- Run gates. Execute the normal test, lint, type, static-analysis, dependency, and secret checks; use fuzzing or differential checks where the risk warrants them.
- Review delivery controls. Inspect CI permissions, secrets, network access, action pinning, logging, encryption, and deployment approvals.
- Document findings. Record defects, accepted risks, required follow-ups, and why the final behavior meets the contract.
- Approve only with understanding. Merge when the accountable reviewer can explain the implementation and its evidence.
Common failure patterns and fixes
“All tests pass, so it is safe.”
Passing tests show only that the executed cases passed. Compare the test cases with the requirement, then add independent negative and boundary cases. A test suite that never exercises a denied request cannot validate authorization.
“The scanner found no issue.”
Scanners are strongest on known patterns and weaker on application-specific intent. Manually inspect business rules, data ownership, trust boundaries, and failure behavior.
“The package name looks right.”
Verify the registry, maintainer, history, license, resolved version, and install behavior. Reject a dependency that cannot be identified confidently.
“The generated test matches the implementation.”
That may be circular validation. Derive cases from the requirement or an independent reference and include inputs the implementation may not have anticipated.
“It only changes CI configuration.”
Workflow and deployment files can widen privileges or expose secrets without touching application code. Review effective permissions, execution context, network access, and approval gates.
“The code is too complex to explain quickly.”
Pause the merge. Ask for a simpler design, a focused refactor, or a walkthrough that establishes invariants and failure behavior. Unexplained complexity is a review risk regardless of who generated it.
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Teams can improve review quality by tracking where defects are found and which paths receive the required scrutiny. Useful measures include findings caught during review versus after release, coverage of sensitive-path reviews, review latency for high-risk changes, recurring defect categories, and the proportion of recurring findings converted into automated checks.
Use these measures to improve controls rather than to reward speed alone. A lower review time is not an improvement if critical findings move downstream. When a repeated mistake becomes a rule, test, scanner check, or code-owner boundary, future AI-generated changes become easier to evaluate without removing human responsibility.
What “ready to merge” means
An AI-generated change is ready only when its scope is known, its behavior has been traced, dependencies are verified, tests have been challenged, automated gates have passed, sensitive areas have received the required review, and an accountable human can explain and defend the decision. The origin of the code changes how carefully you should question it—not who is responsible for the result.
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