The Tool Desk
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An agent must never approve, merge, sign, or deploy artifacts it generated. If the reviewer cannot explain what changed, why it is correct, what evidence covers it, and who is accountable for the decision, the change is not ready.
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Why AI-generated code needs a different review discipline
Autonomous coding agents are productive because they can inspect a repository, edit multiple files, run tools, and iterate without a person approving every keystroke. That autonomy also changes the failure model. The agent may misunderstand an acceptance criterion, infer an unsafe default, omit a negative test, copy a vulnerable dependency pattern, or report a command as successful when it ran against the wrong fixture or environment.
The central control is separation of duties. Generation, review, approval, merge, signing, and deployment should be distinct actions performed by separately governed identities or systems. A model instruction such as “do not deploy” is useful guidance but is not an enforcement mechanism. Repository permissions, CI policy, protected branches, deployment gates, and identity controls must make the unsafe path unavailable or auditable.
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Do not treat an agent as malicious by default, but do treat its output as untrusted until evidence establishes otherwise. The same standard applies whether the agent is operated by a developer, a platform team, or a vendor.
Set a change contract before the agent starts
Write down the boundaries of the task before generation begins. This prevents the agent from quietly redefining “done” and gives the reviewer a testable basis for approval.
- Issue and acceptance criteria: State the user problem, required behavior, non-goals, compatibility expectations, and observable acceptance tests.
- Affected components: Identify services, libraries, schemas, build files, infrastructure, client applications, and documentation that may change.
- Threat model: Record relevant attackers, trust boundaries, abuse cases, authentication and authorization assumptions, and security invariants.
- Data sensitivity: Classify credentials, personal data, payment information, regulated records, production data, and proprietary source that the task could expose.
- Rollback plan: Specify how to revert code, database changes, configuration, feature flags, and external side effects. A rollback that cannot undo a migration needs a forward-fix plan.
- Risk tier: Set the required reviewers, tests, permissions, and rollout method before the agent works. The agent must not be able to lower the bar by describing a high-impact change as “small.”
Keep the contract with the pull request or task record. When the agent proposes a change outside the stated scope, stop and amend the contract deliberately rather than accepting scope drift.
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Use the smallest set of permissions that allows the task to be completed. OpenAI describes sandboxing, network policies, approval decisions, and agent-aware logs as controls for real deployments. Anthropic describes Claude Code as read-only by default and requiring human approval before actions that modify code or systems. Defaults can change, so verify the configuration of the product and version you actually operate.
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| Control area | Safer default | What requires an explicit approval boundary |
|---|---|---|
| Filesystem | Work in an isolated checkout with access limited to the repository and temporary build directories. | Reading home directories, shared workspaces, credential stores, or unrelated repositories. |
| Network | Deny by default; allow only documented package registries, test services, and source hosts. | Arbitrary outbound requests, webhooks, cloud APIs, or uploading source and logs. |
| Secrets | Keep secrets outside the workspace; inject short-lived, scoped credentials only when unavoidable. | Production keys, signing keys, broad cloud roles, database administrator access, or long-lived tokens. |
| Source control | Allow a new branch and pull request; restrict force-push and protected-branch operations. | Changing branch protections, bypassing required checks, signing releases, or merging. |
| Systems | Use disposable test environments and non-destructive fixtures. | Production access, destructive commands, infrastructure changes, data deletion, or customer-facing operations. |
| Dependencies | Use approved registries, lockfiles, and vulnerability policy. | Publishing packages, changing registries, adding unreviewed install scripts, or upgrading critical runtime components. |
Make approvals specific. “Allow the agent to use the network” is too broad; “allow read-only access to this package registry for this run” is reviewable. Log the request, the decision, the identity that approved it, and the resulting action. If a task needs a permission that cannot be safely scoped, move that step to a human-operated process.
Require a review packet from the agent
Before a person reviews the diff, require a concise packet attached to the pull request. Treat every statement in it as an assertion to verify, not as evidence by itself.
- Summary: Explain the behavior added, changed, or removed in user terms.
- Files and systems changed: List source files, tests, configuration, schemas, generated artifacts, dependencies, and deployment manifests.
- Assumptions: Identify interpretations of ambiguous requirements, compatibility decisions, feature-flag behavior, and expected operational conditions.
- Tests run: Give the exact commands, commit or revision, environment, fixtures, scope, duration where useful, and result for each check.
- Known failures and skipped work: Name failed, flaky, unavailable, or intentionally skipped tests and explain why they do not invalidate the change—or state that approval is blocked.
- Dependency and schema impact: Describe new packages, version changes, lockfile updates, migrations, indexes, data backfills, and downgrade implications.
- Security-sensitive paths: Call out authentication, authorization, input handling, secrets, logging, cryptography, file access, deserialization, network calls, and privilege changes.
- Rollback steps: Provide tested or explicitly untested reversal instructions, including database and configuration actions.
A packet that omits the exact revision or command is incomplete. “Tests passed” has no useful meaning if a reviewer cannot reproduce which tests ran against which code and environment.
Review the diff independently
Read the proposed change as if the agent’s explanation did not exist. Start with the contract, then inspect the implementation, tests, and generated artifacts.
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Check requirement coverage and scope
- Map every acceptance criterion to code and a meaningful test or other evidence.
- Look for unrequested behavior, broad refactors, renamed interfaces, feature flags left permanently enabled, and configuration changes hidden in generated files.
- Confirm compatibility with supported runtimes, clients, API versions, and deployment topology.
- Verify that error messages, status codes, retries, timeouts, and fallback behavior match the contract.
Inspect control flow and failure handling
- Trace success, validation failure, dependency failure, timeout, cancellation, retry, and partial-completion paths.
- Check resource cleanup, transaction boundaries, idempotency, concurrency, locking, queue visibility, and duplicate-event handling.
- Ensure exceptions are neither swallowed nor exposed as sensitive diagnostics. Confirm that logs contain enough context to operate the feature without recording secrets or personal data.
- Review migrations and startup behavior for ordering, locking, backward compatibility, and safe recovery if deployment stops halfway.
Review security boundaries
- Verify authentication before authorization and authorization at every object or action boundary, not only at the route or user-interface layer.
- Test untrusted input for injection, path traversal, unsafe deserialization, server-side request forgery, cross-site scripting, and resource exhaustion where applicable.
- Check secret handling, token scope, encryption use, key rotation assumptions, and redaction in logs, traces, errors, and telemetry.
- Inspect dependency provenance, install scripts, transitive packages, lockfile changes, and build or release workflow modifications.
Evaluate maintainability and accessibility
AI-generated code often looks polished while encoding unnecessary complexity. Prefer the project’s established patterns, clear names, small interfaces, and documentation of non-obvious invariants. Check keyboard access, labels, focus behavior, contrast, localization, and screen-reader semantics for user-interface changes. Generated code should be easy for the next human maintainer to reason about, not merely easy for the current agent to produce.
Examine the tests themselves
Count assertions, not test files. A test that only checks a response is non-empty may pass while authorization is broken. Look for boundary values, malformed input, permission failures, dependency errors, retries, concurrent requests, migration states, and regression cases tied to the original defect. Confirm that fixtures are isolated and that tests would fail if the implementation were deliberately weakened.
Run deterministic gates that match the risk
At minimum, reproduce the project’s formatter or linter, build or type checks, unit tests, integration or contract tests, and security scanning. Add checks according to the change rather than treating a green unit-test job as universal evidence.
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|---|---|---|
| Formatting and linting | Exact CI command, revision, configuration, and clean result. | Always; add repository-wide checks when generated files or shared rules changed. |
| Build and type checks | Target platform, compiler or runtime version, warnings policy, and artifacts produced. | Required for public interfaces, build scripts, generated code, and dependency upgrades. |
| Unit tests | Test scope, fixtures, failures, skips, and meaningful assertion coverage. | Add property-based or fuzz tests for parsers, validators, protocol handlers, and security-sensitive logic. |
| Integration or contract tests | Service versions, databases or queues used, migration state, and environment configuration. | Required for API, schema, authentication, storage, messaging, and cross-service changes. |
| Security checks | Static analysis, dependency scan, secret scan, container or infrastructure scan, and resolved findings. | Strengthen for auth, payments, secrets, infrastructure, release workflows, and externally reachable code. |
| End-to-end and operational checks | Scenario, test data, observability signals, rollback result, and cleanup status. | Use for customer journeys, production configuration, migrations, and high-blast-radius changes. |
Run the same checks in CI on the proposed revision, not only on the agent’s workstation. Record failed and skipped tests instead of hiding them with exclusions. Coverage can reveal untouched paths, but a percentage alone does not demonstrate authorization, supply-chain, migration, or operational safety.
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Set approval tiers by risk
Choose the tier before generation using six dimensions: blast radius, reversibility, data sensitivity, privilege, external side effects, and evidence quality. A small diff can still be high risk if it changes a release workflow or authorization check.
| Change profile | Typical examples | Minimum control pattern |
|---|---|---|
| Routine, low impact | Documentation, isolated tests, or a local refactor with no runtime or dependency effect. | Normal CI, one qualified reviewer, and confirmation that scope stayed isolated. |
| Moderate impact | Product behavior, public API changes, dependency updates, or shared library code. | Reproducible unit and integration evidence, focused security review, and an owner familiar with the affected component. |
| High impact | Authentication, authorization, payments, secrets, infrastructure, data migrations, or build and release configuration. | Narrow permissions, independent security checks, code-owner approval, migration or rollback evidence, and staged rollout. |
| Critical production action | Direct production changes, signing, deployment, destructive data operations, or broad privilege changes. | Agent prohibited from the action; separate human-controlled approval and deployment identity, canary or staged release, live monitoring, and an explicit rollback decision. |
Low evidence raises the tier. If tests cannot run, a dependency cannot be verified, or the environment differs materially from production, reduce scope, add safeguards, or defer the merge rather than granting an exception by default.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Enforce approval and merge boundaries in source control
Put the policy in repository settings and CI. On GitHub, protected branches can require pull-request reviews, required status checks, conversation resolution, signed commits, successful deployments, and restrictions on who may bypass rules. GitHub rulesets can also require code-scanning and secret-scanning resolution, code-quality results, and coverage restrictions. Configure these controls for the repositories and branches that matter; do not assume an organization-wide default covers every path.
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- Require at least one independent reviewer, and use code-owner approval for sensitive directories.
- Dismiss stale approvals after substantive changes so an approval cannot survive a materially different diff.
- Require all status checks on the exact commit being merged; prevent a branch update from invalidating the tested revision.
- Restrict bypass permissions to a small, named group and log every bypass with a reason.
- Block direct pushes to protected branches and prevent the agent identity from approving, merging, signing, or deploying its own work.
- Require successful deployment checks before promotion when the repository uses environment gates.
OWASP AISVS Appendix C states: “Verify that autonomous agents cannot approve, merge, sign, or deploy artifacts that they themselves generated.” This is a separation-of-duties requirement, not a suggestion to add another prompt to the agent.
Stage releases and observe the result
After approval, merge only when required checks and reviews are green. For production-impacting changes, release progressively: a canary, one region, a small tenant cohort, or a feature-flagged audience. Define the stop conditions before rollout, including error-rate, latency, authorization failure, data-integrity, queue, and security signals.
Keep operators able to halt promotion without asking the agent. Watch dashboards, logs, traces, audit events, and security alerts during the observation window. Validate the intended behavior with a production-safe smoke test, then confirm that rollback or forward-fix procedures work. Retain the prompt or task specification, tool activity, diff, exact test results, approvals, deployment decision, and post-release observations according to your audit policy.
What to do when evidence is missing or a check fails
Fail closed. Do not convert an unexplained failure into a warning merely because the agent says the change is safe.
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- Stop the merge or deployment. Keep the revision identifiable and preserve the failing logs.
- Classify the gap. Decide whether it is a code defect, flaky test, unavailable dependency, environment mismatch, insufficient permission, or missing requirement.
- Reduce uncertainty. Ask for a focused reproduction, add a regression test, run the check in CI, or narrow the diff. Do not let the agent edit the test only to make it pass without reviewing the intended assertion.
- Reassess risk. A new migration, privilege change, dependency, or external side effect can move the change into a higher approval tier.
- Record the decision. Document the failure, evidence gathered, owner, approved exception if any, expiry date, and follow-up action.
- Retry from a clean revision. After substantive changes, rerun required checks and obtain fresh approvals; never rely on an approval for an earlier diff.
How to interpret claims about autonomous review automation
OpenAI Alignment reported an internal Auto-review comparison in 2026 with roughly 200 times fewer synchronous approval interruptions. The same internal snapshot reported that around 99% of reviewed boundary-crossing actions were auto-approved, with 720 actions manually interrupting the user versus seven rejected by Auto-review; four of those seven continued through safer paths and three stopped for user input. These figures depend on sandbox configuration and use case, describe an illustrative internal deployment snapshot, and are not defect-detection, code-quality, or safety pass rates.
No universal statistic establishes that autonomous review catches a particular share of defects or makes software more productive. Evaluate your own controls using reproducible evidence: escaped defects, rollback frequency, security findings, review latency, false approvals, bypass events, and the proportion of changes with complete test and audit records.
A practical approval checklist
- The change contract names the requirement, affected components, threat model, data sensitivity, risk tier, and rollback plan.
- The agent ran with isolated filesystem and network access, no unnecessary secrets, and explicit approval boundaries for external or destructive actions.
- The review packet lists the exact revision, files, assumptions, commands, environments, results, failures, skips, dependencies, security paths, and rollback steps.
- An independent reviewer inspected the diff, tests, generated artifacts, configuration, migrations, authorization, error handling, and operational behavior.
- Required formatter, linter, build or type checks, unit tests, integration or contract tests, and security scans passed on the proposed revision.
- Additional fuzz, dependency, secret, migration, end-to-end, or infrastructure checks were added when the risk warranted them.
- Protected-branch or ruleset controls require the appropriate reviews and checks, dismiss stale approvals, and restrict bypasses.
- The generating agent cannot approve, merge, sign, or deploy its own artifact.
- Production changes use a staged rollout, defined stop conditions, monitoring, and a tested recovery path.
- Prompts, tool activity, diffs, test evidence, approvals, exceptions, and deployment decisions are retained for audit.
Governance references to align your policy
NIST published SP 800-218A — Secure Software Development Practices for Generative AI and Dual-Use Foundation Models on July 26, 2024. It augments SSDF 1.1 with AI-specific practices and tasks across the software development life cycle. Use it alongside your existing secure-development standard rather than treating agent use as a replacement for one.
OpenAI describes the governance problem this way: “Security teams need ways to govern how agents operate: what they can access, when human approval is required, which systems they can interact with, and what telemetry exists to explain their behavior.” That framing maps directly to repository permissions, approval gates, environment controls, and audit logging.
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