AI for Architecture Review: Check Assumptions and Tradeoffs

AI for architecture review works best when you ask an assistant to test an existing software design against explicit requirements and supporting evidence. Give it the design, the constraints, and the questions a reviewer must answer, then verify its findings before accepting changes.

A persuasive paragraph is not proof that a system is reliable or secure. Tools like fenbs keep the accepted decisions and remaining review work outside the conversation, so the next assistant can distinguish an approved tradeoff from an unresolved concern.

How do you start a useful review?

1. Name the decision under review: for example, whether an existing order-processing design can support a new customer workflow.
2. Provide a dated evidence pack with requirements, diagrams, relevant code locations, operational measurements, and earlier decisions.
3. Ask the assistant to identify gaps before judging the design. Missing facts should become questions, not guessed answers.
4. Review one concern at a time, such as access control, failure recovery, data consistency, or operating effort.
5. Turn verified findings into accepted risks, follow-up tasks, or a request for more evidence. Keep a person responsible for the final decision.

Set the boundary before supplying a large repository. A review of payment retries does not need an opinion on every screen. State what can change, what must remain compatible, and what would make the proposal unacceptable. This keeps the response connected to the actual decision.

The AWS Well-Architected Framework↗ (opens in a new tab) describes architecture review as a constructive conversation about decisions. That is a useful stance here: the assistant helps surface questions; it does not award a universal pass or fail.

What evidence should the assistant see?

Use a small, readable pack with named sources. Include the system purpose, the people and systems that use it, major data movements, and the requirements that matter to this review. Mark each file or excerpt with its revision so an observation points to the design being evaluated.

A diagram helps when its boundaries and arrows have clear meanings. The C4 model diagram guidance↗ (opens in a new tab) offers different views for different audiences; use the views that explain the question rather than generating every possible diagram. Add a short text description of important flows so visual ambiguity does not become a false finding.

Keep credentials and unnecessary customer data out of the pack. Use approved, redacted examples and give the assistant only the access the review requires. The AI assistant access review guide covers the separate question of reviewing the assistant’s own permissions.

How do you ask for findings instead of opinions?

Require a consistent finding format: requirement, evidence, concern, consequence, and next check. Ask the assistant to label unsupported statements as assumptions. A concern should explain a plausible failure path rather than attach a strong adjective to a component.

Review the supplied order-processing design against requirements R1-R4.
Do not redesign the whole system.
For each concern, return:
- requirement and source reference
- observed design behavior
- possible failure and affected user
- evidence still missing
- smallest check that would resolve the concern
Separate confirmed findings from hypotheses.
Do not treat an earlier proposal as an approved decision.

Say, for example, that an order may be submitted again after a network timeout. A useful finding identifies where duplicate processing could occur and asks for evidence that repeated submissions produce one business result. It does not immediately prescribe a queue, a new database, or a larger rewrite.

Request an opposing reading after the first pass: what evidence supports the current choice, and when would keeping it be reasonable? This can reveal whether the assistant is criticizing a real weakness or applying a preference that does not fit your workload.

Which conclusions need a separate check?

Check every claim that depends on behavior outside the supplied material. The assistant cannot establish recovery time from a diagram or prove isolation between customers from a component name. Ask the responsible person to inspect implementation details, run an appropriate test, or supply the missing record.

• For a code claim, open the cited location and confirm that the relevant path actually runs.
• For a performance claim, define the workload and measure the part of the system involved.
• For a vendor capability, read the vendor’s current documentation and check the configuration you use.
• For a security concern, confirm the trust boundary and the enforcement point with a qualified reviewer.

Architecture review considers system boundaries and tradeoffs. AI code review tools can help inspect implementation changes, but a clean change review does not resolve an architectural question about the whole workflow. If your system itself uses autonomous software, the AI agent architecture guide addresses that separate design subject.

How do you record accepted and rejected findings?

Give each verified concern a disposition: change required, risk accepted, rejected with evidence, or unresolved. Record the person who chose, the reason, and the scope. Accepting a slower report today does not mean every future operation can ignore response-time requirements.

Preserve rejected options when their reasoning would help another review. “Separate service rejected because the current team cannot support another deployment” is more useful than a bare rejection. State what change in circumstances would justify reopening the choice.

For continuing work, fenbs Decisions and rules keep the human choice beside follow-up tasks, so a later assistant does not quietly reopen settled tradeoffs.

How to do this with fenbs

1. Create a free account and add the software project to your kanban board.
2. Add review tasks with the requirement, evidence reference, and planned verification. Keep long design documents in their existing home and record their links.
3. Record the reviewer’s choices in Decisions and rules. Keep unanswered questions open; assistants can record what a person decided but cannot decide or sign off.
4. Connect a supported assistant through Integrations. Ask it to read the project context first and save progress with the next unresolved check before stopping.

fenbs does not inspect your deployment or run architecture tests. If you need a live infrastructure assessment, use the appropriate engineering tools and reviewers; use fenbs when the missing piece is a durable record of the decisions and work.

When should you repeat the review?

Repeat it when a meaningful premise changes: a new data category, a different load pattern, a new integration, or a changed recovery requirement. Start with the earlier decision and its trigger for reconsideration. Reuse the evidence that still applies and replace what has become stale.

Before closing the session, ask someone to read the findings without the chat. They should be able to locate the evidence, understand what was accepted, and identify the next check. The context engineering hub explains the broader practice of supplying that useful starting context.