On this page#
What changes when an agent does this#
Amazon mandating AI code review would not remove the need for human review; it would move the bottleneck from finding possible defects to judging whether machine-generated findings are relevant, complete, and safe to act on. The workable model is simple: let the agent inspect broadly, require evidence for every claim, and prevent it from approving or merging its own changes.
Code review is the systematic examination of source code, usually intended to find defects and improve maintainability. An AI agent can accelerate that examination because it can trace call sites, compare related files, and produce a first-pass risk inventory without waiting for another engineer.
But faster review is not automatically better review. The scarce resource becomes disciplined human attention.
The agent should handle:
- Mapping the changed surface area.
- Checking explicit repository invariants.
- Looking for missing tests and unsafe error paths.
- Producing evidence-linked findings.
- Proposing narrowly scoped repairs.
The human should retain:
- Deciding whether the stated invariant is correct.
- Judging product and operational consequences.
- Rejecting speculative findings.
- Approving security-sensitive changes.
- Authorizing the merge.
This boundary matters because a large language model generates outputs from learned statistical patterns; it does not independently guarantee that a repository-specific claim is true. Treating fluent output as proof is the central mistake in AI review programs.
The right acceptance line is therefore not “the agent found no issues.” It is: “Every material change has been checked against named invariants, and a human has reviewed the evidence.”
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The prompt chain#
A reliable review uses several constrained prompts, not one request to “review this PR.” Each stage should produce an artifact that the next stage can challenge. This separation prevents the agent from silently mixing repository discovery, defect detection, repair, and self-approval into one confident narrative.
Tools such as Claude Code are designed to work with codebases and development workflows; its official documentation is the appropriate reference for supported behavior. The repository still has to supply the rules the model cannot infer safely.
Stage 1: Build the change map#
First, make the agent identify what changed and what could be affected. Do not ask for defects yet; premature diagnosis encourages it to anchor on the first plausible explanation.
Inspect the proposed change and produce a change map.
For each changed symbol:
1. State its role.
2. List direct callers and consumers visible in the repository.
3. Identify affected tests, schemas, configuration, and public interfaces.
4. Name any repository invariant that may be touched.
5. Cite the file and symbol supporting each claim.
Do not suggest fixes. Mark anything not established by repository evidence as UNKNOWN.“UNKNOWN” is deliberate. It gives the agent a valid alternative to invention. Requiring file-and-symbol evidence also makes the output reviewable instead of merely persuasive.
Stage 2: Review against invariants#
Next, ask for findings only where the change conflicts with an explicit rule, an established behavior, or a demonstrated failure path. A useful finding must include consequence and evidence.
Review the change map against these invariants:
- [insert repository invariants]
- [insert API compatibility rules]
- [insert authorization and data-integrity rules]
Return only findings that include:
1. The violated invariant.
2. The exact execution path.
3. The observable consequence.
4. Repository evidence.
5. A test that would fail before the repair and pass after it.
Separate CONFIRMED findings from RISKS. Do not present a risk as a defect.This is where most teams become too permissive. A style preference is not a correctness failure, and a theoretically possible path is not a confirmed bug. Traditional code review already suffers when signal is buried under low-value commentary; automation can multiply that problem instantly.
Stage 3: Challenge the findings#
The same agent should be required to argue against its initial conclusions before any code changes. This is not independence, but it forces an explicit search for disconfirming evidence.
Act as a skeptical reviewer of the findings above.
For each finding:
1. Identify the strongest reason it may be wrong.
2. Search for guards, validation, wrappers, or tests that invalidate it.
3. Downgrade it if the execution path is not demonstrated.
4. State what evidence is still missing.
Delete findings that cannot survive this challenge.
Do not create new findings in this stage.A finding that disappears under basic challenge never deserved human attention. For a reusable version of this staged workflow, PairFoundry’s Review & Repair pack is the direct next step; engineers who want the underlying method first can use the free foundations track.
Stage 4: Propose the smallest repair#
Only confirmed findings should reach repair. The agent must preserve unrelated behavior and state exactly what the patch will not change.
For each confirmed finding, propose the smallest repair.
Include:
1. Files and symbols to change.
2. The invariant restored.
3. The regression test to add or modify.
4. Behaviors explicitly left unchanged.
5. Any migration, compatibility, or rollback concern.
Do not edit code until a human approves the repair plan.
Do not combine unrelated cleanup with the repair.This constraint blocks opportunistic refactoring. A review fix that rewrites adjacent code creates a larger verification problem than the original defect.
The gate#
The mandatory gate is a human review of the surviving findings and proposed patch before execution or merge. The reviewer must stop, inspect the cited code, confirm the invariant, and decide whether the regression test proves the claimed failure. Clicking “accept all” is not a gate; it is delegated authority disguised as oversight.
Use a compact decision table:
| Question | Required answer | |---|---| | Is the invariant explicit and valid? | Yes, with its repository source | | Is the execution path demonstrated? | Yes, or the finding remains a risk | | Is the consequence material? | Yes, stated in observable terms | | Does the test reproduce the failure? | Yes, without depending on the repair | | Is the patch narrowly scoped? | Yes, with unrelated changes removed | | Can it be rolled back cleanly? | Yes, with a named boundary |
The agent must never approve its own repair. An LLM can repeat the same mistaken assumption across review, implementation, and validation, so multiple agent passes do not equal independent verification.
Teams adopting a broader agent workflow can compare PairFoundry’s three packs or browse the workflow guides. Regardless of tooling, the non-negotiable control is human ownership of the merge decision.
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What it gets wrong#
AI review fails predictably: it invents repository rules, misses behavior outside the visible context, overstates speculative risks, and accepts tests that merely confirm its own implementation. These failures are recognizable when reviewers demand traceable evidence instead of judging the confidence or polish of the response.
Watch for these patterns:
- Phantom invariant: The agent says code “must” behave a certain way but cannot point to a test, contract, schema, or established caller.
- Context truncation: It reviews the diff while missing generated code, configuration, migrations, feature flags, or indirect consumers.
- Plausible execution path: The described bug sounds coherent, but an upstream guard makes the path unreachable.
- Severity inflation: A maintainability preference is labeled a production defect.
- Self-fulfilling test: The new test encodes the proposed implementation rather than an externally observable requirement.
- Repair drift: A two-line defect becomes a cross-file refactor.
- False closure: “No issues found” is treated as evidence of safety.
The detection rule is blunt: if a finding cannot name the violated invariant, reachable path, observable consequence, and supporting location, it is not ready for action.
Official tool documentation can explain capabilities and operation, but it cannot define your repository’s business invariants. Those belong in version-controlled instructions, tests, schemas, and review checklists. Assuming the model will infer them correctly is a bad engineering practice.
Rollback#
Rollback should restore the last human-approved state without preserving agent-generated cleanup, formatting, or bundled refactors. Keep each repair isolated, record the invariant it addresses, and make the regression test separable from the implementation so the team can distinguish a reverted fix from a discarded requirement.
Use this sequence:
- Stop automated follow-up edits and merging.
- Identify the exact repair boundary.
- Preserve the failing test if it expresses a confirmed invariant.
- Revert the implementation changes within that boundary.
- Re-run the repository’s required checks.
- Reassess the original finding from repository evidence.
- Submit a smaller replacement patch or close the finding as unsupported.
Do not ask the same agent to “undo whatever went wrong” without specifying the approved baseline. That invites another interpretive rewrite. Clean rollback depends on small patches, explicit ownership, and reviewable history—the same properties that make human code review effective.
Photo by Daniil Komov on Pexels.
Related reading#
- Spec driven development: the prompt chain we actually run
- Claude Code planning mode: making the output reviewable, not just correct
FAQ#
How do I opt out of Amazon AI?#
For an internal Amazon development policy, use the documented company exception or escalation path; this article cannot establish that process. Do not bypass a mandated control locally. If the concern is repository safety, request a workflow where AI produces advisory findings while a human retains approval and merge authority.
What is Amazon AI code review policy?#
The phrase “amazon mandating ai code review” does not, by itself, define a complete or authoritative policy. The safe operational interpretation is mandatory AI-assisted review with evidence requirements, explicit human approval, and rollback controls. Employees should rely on the current internal policy text for scope, exceptions, data handling, and enforcement.
Does Amazon use AI to code?#
This article does not make a sourced claim about Amazon’s internal coding practices. It explains how an AI-assisted code-review mandate should be implemented in a real repository: staged prompts, repository-grounded findings, a human gate, and isolated rollback.
How can I turn off AI on Amazon?#
That question usually concerns consumer Amazon services, not software code review. Controls vary by product, so use the settings and official support material for the specific Amazon service. Turning off a consumer AI feature is separate from complying with an employer’s engineering-review policy.
Why are people getting rid of Alexa?#
That is a consumer-device question and is outside the scope of AI code review. Reasons would require current evidence that is not provided here, so they should not be inferred from an engineering mandate or from general concerns about large language models.