Architecture Review Checklist for AI-Assisted Software Delivery

Use the Review to Make a Decision

AI can help trace a codebase, draft an implementation, propose tests, and revise a change quickly. The architecture review should not repeat that work. Its purpose is to decide whether the proposed change belongs in the system and whether the available evidence supports releasing it.

Begin the review with five facts:

• the intended user and business behavior
• the systems and clients that may be affected
• the assumptions made by the implementation
• the evidence already collected
• the person who owns the release decision

If those facts are unclear, the right next step is investigation rather than a larger generated diff. A bounded dependency trace can establish the affected contracts before the implementation expands.

1. Contracts and Consumers

Review what crosses each boundary: API payloads, database records, events, files, SDK calls, and user-visible states. Then identify every known producer and consumer of that contract.

• Does the change alter a field, enum, default, or error response?
• Do web, iOS, Android, background jobs, and reports interpret it the same way?
• Can old and new clients operate during a staggered rollout?
• Is compatibility enforced, or merely assumed?

Decision change: a locally correct endpoint may need a compatibility layer or a coordinated release after the review reveals an older client that still depends on the previous response shape.

2. State, Data, and Invariants

Establish the source of truth and the rules that must remain true before, during, and after the change.

• Which system owns the state?
• Can a retry create duplicates or apply the same transition twice?
• How will existing data be migrated, validated, and rolled back?
• What happens when two users or services update the same record?
• Which history, audit, or retention requirements must survive?

Decision change: a proposed one-step update may become an idempotent operation with an explicit migration and rollback plan when the review identifies existing records or concurrent writers.

3. Failure and Recovery

The happy path demonstrates possibility. The recovery path determines whether the feature can be operated safely.

• What can partially succeed?
• Which failures should retry, stop, compensate, or ask for help?
• Can the user resume without repeating completed work?
• What state remains after a timeout, crash, or interrupted deployment?
• How will support identify and repair a failed transaction?

Decision change: a feature may be released behind a flag rather than to everyone when recovery still depends on an untested manual procedure.

4. Security and Access Boundaries

Check the identity and authority behind every read, write, and external call. A request that is valid for one user, tenant, or role may be invalid for another.

• Are authentication and authorization checked at the correct boundary?
• Can identifiers be used to cross tenant or ownership boundaries?
• Are inputs validated before they reach storage or another service?
• Could logs, prompts, analytics, or error messages expose sensitive data?
• Are credentials and permissions limited to what the feature needs?

Decision change: a shared helper may need to remain split when two workflows have different authorization or audit requirements.

5. Operations and Rollout

Review how the change will be configured, deployed, observed, and reversed in the environment where it will actually run.

• What is the safe deployment order across services and clients?
• Which configuration, secrets, queues, indexes, or scheduled jobs are required?
• What metric, log, or trace will show that the feature is healthy?
• What load, rate-limit, latency, or cost boundary should be watched?
• Can the team disable or roll back the change without losing data?

Decision change: an implementation that passes locally may require observability and a staged rollout before it is ready for a production audience.

6. Evidence and Remaining Uncertainty

A passing test is useful evidence, but the review should state what that test proves. Combine evidence from the diff, automated tests, runtime behavior, logs, migrations, and affected clients.

• Which acceptance criteria were exercised?
• Were failure and permission paths tested, not only the happy path?
• Was the running system inspected where static review was insufficient?
• Were all affected platforms or consumers checked?
• What remains unverified, and who accepts that risk?

The AI-assisted code review guide goes deeper on comparing product intent, the actual diff, tests, and runtime behavior.

The Architecture Review Record

Keep the artifact short enough to update. A useful record can fit into seven fields:

1. Intent: the behavior the change is meant to create.
2. Scope: the systems, clients, and data it can affect.
3. Contracts: the boundaries reviewed and compatibility required.
4. Evidence: tests, runtime checks, logs, or artifacts inspected.
5. Recovery: how failure, rollback, and repair will work.
6. Open questions: uncertainty that remains before or after release.
7. Decision: accept, revise, stage, or block, with a named owner.

This record changes an architecture review from a general expression of concern into a decision someone can verify later. It also gives the next AI-assisted session better context than the code alone.

A Small Illustrative Decision

Consider a proposed change that adds a status to an API response and a screen that displays it. The generated code compiles, the endpoint test passes, and the new screen works.

The architecture review finds that a background job writes the same status, an older mobile client treats an unknown value as an error, and no metric reports failed transitions. The decision changes from “ship the feature” to “add backward-compatible handling, test both writers, and instrument the transition before a staged release.”

That example is illustrative, not a claim about a specific client. Its purpose is to show the artifact at work: the review connects a plausible implementation to the rest of the system and produces a different, testable release plan.

The Outcome Is a Better Release Decision

Architecture review is not a contest between generated code and human code. It is a disciplined way to decide what evidence is sufficient for this system, this risk, and this release.

Accept the change when its behavior and boundaries are understood. Revise it when the design is sound but the contracts or recovery path are incomplete. Stage it when production evidence is still needed. Block it when access, data integrity, or safe recovery remains unknown.