Software Architecture Review Checklist for AI Codebases

AI-assisted development can accelerate code production, but it does not remove the need for explicit architectural goals, evidence, human judgment, and controlled change. Before scaling a product, planning a major migration, preparing for a critical release, or increasing AI-assisted delivery, engineering leaders need to know whether the existing architecture still supports the business and quality requirements. This software architecture review checklist explains what to inspect, what evidence to collect, how to assess architectural risks, and how to turn findings into a prioritized remediation roadmap.

What Is a Software Architecture Review?

A software architecture review is a structured evaluation of whether a system’s architecture is fit for its business objectives, quality requirements, operational context, and constraints. It focuses on architectural decisions and their consequences rather than reviewing individual lines of code.

A review typically has four elements:

• Inputs: business goals, quality-attribute scenarios, architecture documentation, source-code structure, deployment information, operational evidence, and ownership data.
• Participants: an architect or technical lead, engineering representatives, product or business stakeholders, and specialists when the risk profile requires them.
• Assessment: evaluation of architectural decisions against prioritized scenarios, constraints, tradeoffs, and available evidence.
• Outputs: documented findings, risks, supporting evidence, owners, proposed actions, and a remediation or validation plan.

The distinction from other technical assessments is important.

Activity	Primary focus	Difference from an architecture review
Code review	Implementation correctness, local design, maintainability	Examines specific code changes rather than system-wide architectural fitness
Security audit	Vulnerabilities, controls, threats, security requirements	May require specialist testing and evidence beyond architectural assessment
Performance testing	Measured behavior under defined workloads	Produces empirical workload evidence; architecture review evaluates whether the design supports required scenarios
Reliability assessment	Failure behavior, resilience, recovery	May involve operational testing and incident analysis beyond architecture decisions
Compliance assessment	Regulatory or contractual requirements	Determines compliance against applicable obligations; architecture review identifies relevant architectural implications
M&A technical due diligence	Technology and transaction risk	Evaluates technology as part of an investment or acquisition decision

An architecture review can identify where specialist validation is needed, but it should not be treated as a replacement for it. A suspected implementation defect may require a focused code review; an uncertain capacity assumption may require performance or load testing; unresolved failure and recovery questions may require a reliability assessment; a material security concern may require a security assessment; and regulatory requirements may require a formal compliance review. The architecture review determines where these deeper assessments are warranted – it does not perform them.

What Evidence Should Be Collected Before the Review?

A software architecture assessment is only as strong as the evidence behind its findings. Before the main review, collect enough information to compare the intended architecture with the system that actually exists.

Evidence-readiness checklist

• Business objectives, constraints, and major upcoming changes are documented.
• Critical quality-attribute scenarios are defined and prioritized.
• Current system-context and component diagrams are available.
• Major architecture decisions and tradeoffs are recorded in ADRs or an equivalent decision record.
• Major dependencies and their owners are known.
• Key data flows and data ownership are documented.
• Deployment topology and meaningful environment differences are understood.
• Relevant observability data, incidents, and operational history are available.
• Test strategy and representative test evidence are available.
• Component, service, and operational ownership is clear.
• Material AI-assisted changes can be identified and traced through the normal development workflow.
• Review, testing, approval, and traceability controls for AI-assisted changes are understood.
• Known technical debt and previously accepted architectural risks are documented.

The evidence should cover both the architecture as designed and the architecture as operated. If a diagram says that one service owns a data domain but several services actually write to the same data, that discrepancy is itself a review finding.

The goal is not to collect every possible artifact. It is to obtain enough reliable evidence to test the architectural assumptions that matter to the business.

Software Architecture Review Checklist

Use the following matrix as a practical review framework. It is a starting point, not a universal scoring system. Criteria and weights should reflect business criticality, workload, product lifecycle, regulatory context, and risk tolerance.

Dimension	Key questions	Evidence	Red flags	Owner
Business fit & quality attributes	Does the architecture support the product’s most important business scenarios? Are tradeoffs explicit?	Business goals, quality scenarios, ADRs	Technical decisions cannot be linked to business priorities	Product + Architecture
Boundaries, coupling & maintainability	Are responsibilities clear? What is the blast radius of common changes?	Dependency maps, module structure, change history	Cyclic dependencies, duplicated logic, unclear ownership	Engineering
Data, scalability & performance	Can critical workloads scale within known constraints? Are capacity assumptions supported by evidence?	Data flows, schemas, workload data, test evidence	Shared mutable data, unknown bottlenecks, untested assumptions	Architecture + Engineering
Reliability & operations	How does the system fail, recover, and communicate failure?	Incident records, SLOs, monitoring, runbooks	Missing recovery paths, noisy alerts, unclear operational ownership	SRE/Operations
Security & privacy	Are trust boundaries, authorization, secrets, sensitive data, and dependencies appropriately controlled?	Threat models, access controls, dependency data	Excessive privileges, unmanaged secrets, unclear data classification	Security + Engineering
Deployment & change safety	Can changes be released, observed, and reversed safely?	CI/CD configuration, IaC, migration procedures	Environment drift, irreversible migrations, weak release controls	Platform/DevOps
Documentation & ownership	Does documentation reflect the deployed system, and are decisions and responsibilities discoverable?	Diagrams, ADRs, ownership records, runbooks	Stale diagrams, undocumented exceptions, orphaned components	Architecture + Engineering
AI-assisted change governance	Can AI-assisted changes be reviewed, tested, traced, and owned?	PRs, review policies, tests, dependency/license checks	Unreviewed generated code, duplicate abstractions, unclear accountability	Engineering

Each row represents an assessment dimension, not a score by itself. When a concern is identified, create a separate finding that references the relevant dimension and records the evidence, business impact, likelihood, urgency, evidence confidence, remediation effort, owner, and proposed action.

Business Goals and Quality Attributes

Start with the question: What must the product achieve, and which quality attributes are necessary to achieve it?

Translate broad objectives into measurable quality-attribute scenarios.

For example, instead of stating that “the system must scale,” define the workload, triggering event, expected response, and acceptable outcome. During a major business event, the system might need to maintain an agreed response-time target for a critical user journey while meeting availability and operating-cost constraints. The actual targets should come from the product and operational context.

An architecture tradeoff analysis method can help structure this discussion. The SEI’s Architecture Tradeoff Analysis Method (ATAM) connects business drivers with quality-attribute scenarios and architectural decisions to identify risks and tradeoffs. For example, redundancy may improve availability while increasing cost and operational complexity. The objective is not to maximize one quality attribute, but to make tradeoffs explicit and appropriate to the business scenario.

System Boundaries, Coupling, and Maintainability

A software maintainability assessment should focus on how the system changes, not only on whether individual components appear clean.

Ask:

• Can teams identify who owns each major component?
• Are dependency directions intentional?
• Are module and service boundaries aligned with meaningful responsibilities?
• How many components must change for a routine feature?
• Are interfaces stable enough for independent evolution?
• Is business logic duplicated across modules or services?
• Do abstractions solve recurring problems, or do they add unnecessary complexity?
• Can engineers reasonably predict the blast radius of a change?

Red flags include circular dependencies, multiple sources of truth, duplicated business rules, unstable interfaces, and components without clear ownership.

AI-assisted development can make these problems easier to introduce because a generated solution may optimize the immediate task without seeing the broader architectural context. The appropriate response is not to assume AI-generated code is poor quality. It is to review changes in system context and validate that local solutions remain consistent with architectural boundaries.

Data Architecture, Scalability, and Performance

A software scalability checklist should examine both design assumptions and supporting evidence.

Check:

• Data ownership is explicit.
• Consistency requirements are understood for critical workflows.
• Schema evolution has a controlled approach.
• Capacity assumptions are documented.
• Known bottlenecks have evidence behind them.
• Caching decisions correspond to actual workload requirements.
• Queues and asynchronous processing have appropriate backpressure and failure handling.
• The scaling model matches the expected workload.
• Performance-critical paths have relevant test or production evidence.
• Data-intensive operations have clear operational ownership.

Avoid declaring an architecture “scalable” because it uses a particular pattern, platform, or technology. Scalability is demonstrated relative to a workload, constraints, and evidence.

For teams preparing for significant growth, the architecture review should complement broader planning. See how to prepare your technology for scalable growth when the assessment identifies growth-related architectural constraints.

Reliability, Observability, and Operations

Review the architecture through failure scenarios, not only successful execution.

Ask:

• What happens when a critical dependency times out?
• Are retries bounded and appropriate?
• Can failures degrade gracefully where the business permits it?
• What recovery objectives apply to critical workflows?
• Are backups tested and recoverable?
• Are logs, metrics, and traces sufficient to investigate important incidents?
• Is someone explicitly responsible for operating each critical component?

Incident history is especially useful because it can reveal where actual system behavior differs from the intended architecture.

Security, Privacy, and Compliance

An architecture security review should examine how security requirements are represented in the system design.

Check:

• Trust boundaries and sensitive interfaces are documented.
• Identity and authorization flows are understood.
• Least-privilege assumptions are appropriate.
• Secrets have controlled storage and handling.
• Encryption requirements are addressed.
• Sensitive data is classified and its movement is understood.
• Security-relevant actions are auditable where required.
• Third-party and open-source dependencies are assessed for relevant risks.
• Threat-model coverage exists where appropriate.
• Secure-development and change-control evidence is available.

Security practices should be integrated into the software development lifecycle rather than treated only as a final gate. NIST’s Secure Software Development Framework provides practices that can be integrated into SDLC implementations to reduce vulnerabilities, mitigate the impact of undetected issues, and address their root causes.

If the review identifies material security exposure, a qualified security assessment may be required. The architecture checklist does not replace penetration testing, vulnerability assessment, or formal security review.

Deployment, Infrastructure, and Change Safety

Architecture quality also depends on how safely the system can change.

Review whether:

• Environments are sufficiently consistent for the intended delivery process.
• Infrastructure is versioned and reproducible where appropriate.
• CI/CD includes required validation and approval controls.
• Configuration is controlled and observable.
• Database and data migrations have a safe operational path.
• Rollback or recovery strategies exist for critical changes.
• Feature flags are governed and retired when no longer needed.
• Releases generate enough telemetry to detect material problems.
• Change approval is proportional to business and technical risk.

The objective is not maximum process. It is controlled change at a level of governance appropriate to the system’s risk.

For supporting evidence about testing and release confidence, the review can reference the software test strategy without turning the architecture review into a complete QA assessment.

Documentation, Ownership, and Decision Records

An architecture documentation checklist should answer a practical question: Can another qualified person understand why the system looks the way it does and who is responsible for it?

Check:

• System and component diagrams reflect the deployed system.
• Major architectural decisions and rejected alternatives are recorded.
• Important tradeoffs are documented.
• Components have owners.
• Critical data domains have owners.
• Operational knowledge is discoverable.
• Exceptions to architectural standards are visible.
• Documentation has an update trigger or review responsibility.

Documentation does not need to describe every implementation detail. It should preserve the decisions, boundaries, dependencies, and responsibilities that matter for future change.

AI-Accelerated Codebase Risks

AI-assisted development changes the review context because code can be produced faster than architectural knowledge is updated.

The relevant risk is not that AI-generated code is inherently unsafe or low quality. AI-assisted suggestions may have limited context and miss broader architectural issues or introduce inaccurate or insecure code. Accepted output therefore requires human review, testing, and validation against the architecture and codebase. GitHub similarly recommends reviewing and validating generated suggestions for compatibility with the codebase’s architecture and style. 

An AI-assisted codebase review should therefore look for:

• architectural drift caused by locally optimized changes;
• duplicate abstractions that solve problems already addressed elsewhere;
• inconsistent implementation patterns;
• new or unapproved dependencies;
• security or licensing exposure in generated or suggested code;
• incomplete tests;
• documentation that does not match implementation;
• decisions made without sufficient system context;
• unclear human ownership of accepted output.

AI-assisted change governance checklist

• AI-assisted changes remain subject to the same architectural boundaries as other changes.
• Material changes receive human review by someone accountable for the affected area.
• Generated code is validated with appropriate automated and manual tests.
• New dependencies are reviewed for approval, security, maintenance, and licensing considerations.
• AI-generated abstractions are checked for duplication with existing architecture.
• Local optimizations are checked against system-wide patterns and constraints.
• Generated documentation is verified against the actual implementation.
• Sensitive information is handled according to organizational policy when AI tools are used.
• Material AI-assisted changes can be traced through the normal development workflow.
• A human owner is explicitly accountable for the resulting design and behavior.

The useful signal is not how much code an AI tool produced. It is whether the engineering organization maintains context, traceability, validation, and accountability as change velocity increases.

How to Run the Review, Score Findings, and Build a Remediation Roadmap

A repeatable review can follow seven steps.

1. Define scope and stakeholders

Specify the product or subsystem under review, the business decision driving the review, the environments included, and the quality attributes that matter most.

Typical triggers include:

• significant expected growth;
• a major architectural migration;
• a critical release;
• repeated production incidents;
• substantial technical debt;
• rapid increases in AI-assisted development;
• material changes to security, privacy, or compliance requirements.

The core group normally includes an architect or technical lead, engineering representatives, and relevant product or business stakeholders. Add security, reliability, operations, data, compliance, or other specialists when the system’s risks require their expertise.

2. Collect and validate evidence

Use the evidence-readiness checklist before the main assessment.

Do not silently turn missing evidence into an assumption. If a critical capacity claim has no supporting test or production data, record that evidence gap as part of the finding.

Evidence quality should also influence how confidently a finding is prioritized. A documented production incident, reproducible test result, or current deployment configuration provides stronger evidence than an assumption based only on an outdated diagram. When evidence is incomplete, record the uncertainty instead of treating the assumption as fact.

This distinction is particularly important for architecture decisions that involve future workload, reliability, or cost. A design may appear capable of meeting a requirement, but without representative workload evidence the review should treat that conclusion as an assumption to validate rather than as a proven property of the system.

3. Evaluate scenarios and tradeoffs

Evaluate important architecture decisions against prioritized business and quality-attribute scenarios.

The question is not simply, “Is this architecture good?” Instead ask:

• Does this decision support the required scenario?
• What assumptions does it depend on?
• Which quality attributes does it improve?
• Which qualities might it constrain?
• What evidence supports the decision?
• What happens if the assumption changes?

This scenario-oriented approach is consistent with established architecture evaluation practice. SEI’s ATAM materials connect business drivers with scenarios and architectural decisions, then use the analysis to identify risks and tradeoffs.

4. Record findings with evidence

Each finding should contain:

Field	What to record
Finding	The architectural condition or risk
Evidence	Diagram, ADR, code structure, metric, incident, test, configuration, or other supporting material
Business impact	What could happen if the issue remains unresolved
Likelihood	How plausible the adverse outcome is in the relevant context
Urgency	How quickly the risk needs attention
Evidence confidence	How strong and complete the supporting evidence is
Remediation effort	Relative effort, complexity, or coordination required
Owner	Person or team accountable for the next decision or action
Proposed action	Remediate, contain, validate, monitor, accept, or investigate

5. Prioritize risks contextually

Do not create one universal architecture score. A useful contextual risk-prioritization matrix is:

Business impact	Likelihood	Urgency	Evidence confidence	Remediation effort	Suggested treatment
High	High	High	High	Any	Immediate containment/remediation or specialist assessment
High	Medium	High	Medium/High	Low/Medium	Prioritize and validate assumptions quickly
High	Low	Medium	Low	High	Investigate before committing to expensive remediation
Medium	High	Medium	High	Low/Medium	Schedule near-term remediation
Medium	Medium	Low	Medium	Medium	Plan within normal engineering work
Low	Low	Low	Any	Any	Monitor or explicitly accept with an owner

This is a contextual decision aid, not a standardized industry scoring model. Business criticality, workload characteristics, regulatory exposure, and organizational risk tolerance should determine how the factors are weighted.

The same principle appears in Microsoft’s Well-Architected guidance: teams should prioritize practices relevant to their workload and business goals, considering factors such as business criticality, compliance needs, time to market, cost, effort, and tradeoffs. The framework also treats assessment as iterative rather than as a permanent certification.

Remediation effort should inform the response, not reduce the importance of a high-impact risk. Significant risks may require containment, additional evidence, or specialist assessment before full remediation.

Risk priority can change as business conditions, workloads, or regulatory requirements change, so findings should be tied to the scenarios and decisions that motivated the review rather than treated as permanent rankings.

6. Build the remediation roadmap

Convert findings into a sequence of actions rather than a flat technical-debt backlog.

Priority	Finding	Action	Owner	Dependency	Evidence of completion	Review point
P1	Material risk to a critical business scenario	Contain and remediate	Named team	Required architecture/security/platform decision	Defined validation evidence	Targeted re-review
P2	Architectural constraint affecting planned growth	Validate assumption and redesign if needed	Named team	Capacity or workload evidence	Test or production evidence	Re-review
P3	Maintainability or documentation gap	Refactor or document	Named owner	None or low dependency	Updated design + relevant tests	Next review
P4	Low-impact inconsistency	Monitor or explicitly accept	Named owner	None	Decision record	Periodic review

Prioritize changes that reduce multiple risks, and distinguish defects requiring correction from architectural tradeoffs requiring an explicit decision. A deviation from a preferred pattern is not automatically an architecture failure.

Findings that require specialist validation should be routed accordingly: implementation concerns to code review, capacity assumptions to performance testing, failure or recovery concerns to reliability or operations specialists, and material security or compliance concerns to the appropriate specialists.

7. Schedule the re-review

Architecture changes as the product, workload, organization, and technology change.

Schedule a re-review after material architectural changes, major workload changes, significant incidents, or sustained changes in the development model.

The purpose is not to achieve a permanent “approved” score. It is to maintain an architecture that remains aligned with current business requirements, quality scenarios, operational evidence, and accepted risk.

Need an independent view of architecture risks before scaling or accelerating delivery? JetRuby can assess the codebase, validate the architecture, and turn findings into a prioritized improvement roadmap.

FAQ

1. What is a software architecture review?

A software architecture review is a structured assessment of whether a system’s architecture supports its business goals, quality attributes, operational requirements, and constraints. It evaluates architectural decisions, risks, tradeoffs, evidence, and ownership rather than focusing only on individual code changes.

2. What should a software architecture review include?

It should normally cover business alignment, quality attributes, system boundaries, coupling, maintainability, data, scalability, performance, reliability, observability, security, privacy, deployment safety, documentation, ownership, and AI-assisted change governance where relevant.

3. How do you evaluate software architecture?

Start with business goals and measurable quality-attribute scenarios. Collect architectural and operational evidence, evaluate important decisions and tradeoffs against those scenarios, document risks, and prioritize actions according to impact, likelihood, urgency, evidence confidence, and remediation effort.

4. When should a software architecture review be performed?

Common triggers include significant growth, major migrations, critical releases, recurring incidents, substantial technical debt, organizational changes, or rapid increases in AI-assisted development. A review is also useful when architectural decisions have become difficult to explain or ownership is unclear.

5. What is the difference between an architecture review and a code review?

A code review examines implementation changes for correctness, maintainability, security, and related concerns. An architecture review looks across the system to determine whether its structure and decisions remain fit for business and quality requirements.

6. How do you assess software architecture risks?

Identify the risk, document the evidence, describe the potential business impact, assess likelihood and urgency, record confidence in the evidence, estimate remediation effort, assign an owner, and define the next action. High-risk findings may require specialist validation rather than checklist-based judgment alone.

7. How should AI-generated code be reviewed at the architecture level?

Treat AI-assisted output as an engineering change that requires accountable human review. Verify that it follows existing architectural boundaries, does not introduce unnecessary dependencies or duplicate abstractions, has appropriate tests and documentation, and can be traced through the normal development workflow.

8. Who should participate in a software architecture review?

The core group usually includes an architect or technical lead, engineering representatives, and relevant product or business stakeholders. Depending on the system’s risks, security, reliability, operations, data, compliance, or other specialists should participate as needed.