Architecture Considerations

AI-assisted development does not just change how code is written -- it changes what the resulting codebase looks like. When developers generate code at 2-3x the rate they could write it manually, the volume, consistency, and structural patterns of your codebase evolve in ways that require architectural attention. This section addresses the architectural implications of AI-generated code at scale, covering code quality patterns, maintainability concerns, and system design strategies.

How AI Changes Your Codebase

Volume and Velocity Effects

AI-assisted teams produce more code, faster. This creates architectural consequences:

Effect	Architectural Impact	Response
More code, faster	Codebase grows more quickly; entropy increases faster	Strengthen architectural guardrails, enforce consistency
Pattern proliferation	AI may generate subtly different implementations of the same pattern	Establish canonical examples; reference them in prompts
Dependency sprawl	AI suggests new libraries and frameworks liberally	Enforce approved dependency list; require justification for additions
Test volume	AI generates many tests, not all meaningful	Define test quality standards; review AI-generated tests critically
Documentation volume	AI generates verbose but potentially inaccurate documentation	Verify accuracy; prefer concise over comprehensive

Quality Patterns in AI-Generated Code

AI-generated code exhibits specific quality patterns that differ from human-written code:

Positive patterns:

• Consistent formatting and style (follows whatever pattern is in context)
• Comprehensive null checking (when prompted)
• Standard error handling patterns
• Good use of language idioms for common tasks

Negative patterns:

• Plausible incorrectness. Code that reads well but implements the wrong logic
• Shallow abstraction. AI tends to generate concrete implementations rather than well-designed abstractions
• Inconsistent architecture. Different prompts produce different patterns, even for similar tasks
• Over-engineering. AI sometimes generates more complex solutions than necessary
• Dead code. Generated functions or methods that are never called
• Duplicate logic. Similar code generated in different places instead of shared abstractions

Maintainability at Scale

The Consistency Challenge

The most significant architectural risk from AI-assisted development is inconsistency. When multiple developers use AI to generate similar functionality, each prompt produces a slightly different implementation. Over months, this creates a codebase with many variations of the same pattern, making maintenance difficult.

Mitigation strategies:

1. Canonical examples. Maintain a set of "reference implementations" for every major pattern in your codebase. Developers reference these in their prompts (see Prompt Engineering).

2. Architectural decision records (ADRs). Document your architectural decisions and make them accessible to developers. AI-generated code should be validated against these decisions.

3. Automated consistency checks. Use linters and static analysis tools configured with custom rules that enforce your specific patterns, not just generic coding standards.

4. Regular architecture reviews. Schedule monthly architecture reviews where the team examines AI-generated code for pattern drift and inconsistency.

The Comprehension Challenge

When developers generate code faster than they can deeply understand it, the organization accumulates "comprehension debt" -- production code that no individual fully understands.

Mitigation strategies:

1. Code ownership model. Assign clear owners for each module or service. Owners must be able to explain any code in their module, regardless of how it was generated.

2. Architecture documentation. Maintain up-to-date system architecture documentation. AI-generated code should fit within the documented architecture.

3. Mandatory code walkthroughs. For complex AI-generated code, require the author to walk through the implementation with a peer. If they cannot explain it, it should not be committed.

4. Limit generation scope. Establish a policy that AI generation should target individual functions or small modules, not entire subsystems. Larger scopes increase comprehension debt.

System Design Implications

API Design

AI tools excel at generating API implementations but may not make good API design decisions. Protect API design as a human-driven activity:

• API contracts first. Design API contracts (OpenAPI specs, GraphQL schemas) before generating implementations. Use the contract as a constraint in AI prompts.
• Review API surface area. AI may generate endpoints or fields that are not needed. Review API design separately from implementation review.
• Consistency across services. Enforce naming conventions, error formats, and pagination patterns at the architecture level. Do not leave these decisions to AI prompts.

Database Design

AI-generated database interactions deserve special architectural attention:

• Schema design is human work. AI can generate migrations from a design, but the design itself should be deliberate and human-driven.
• Query patterns. AI frequently generates N+1 queries, full table scans, or unbounded results. Monitor query patterns per PRD-STD-003 performance testing requirements.
• Migration safety. AI-generated migrations must be reviewed for reversibility, data safety, and production impact.

Security Architecture

The 2.74x vulnerability rate in AI code means security architecture must be more robust, not less:

• Defense in depth. Do not rely on application-level security alone. Ensure infrastructure-level controls (network segmentation, IAM, encryption at rest) provide independent protection.
• Security by default. Configure frameworks and libraries to be secure by default. AI will follow the path of least resistance -- make that path secure.
• Least privilege. AI may generate code with broader permissions than necessary. Review all IAM roles, database grants, and API scopes per PRD-STD-005.

Architectural Governance for AI Code

Quality Gates

Implement automated quality gates that catch common AI architectural issues:

Gate	What It Catches	Implementation
Dependency check	Unapproved or vulnerable dependencies	Dependency allowlist in CI
Complexity limit	Overly complex AI-generated solutions	Cyclomatic complexity threshold in CI
Pattern check	Deviations from established patterns	Custom linter rules
API contract check	Implementation does not match contract	Contract testing in CI
Dead code detection	Unreferenced functions or modules	Static analysis in CI
Duplication detection	Similar code in multiple locations	Duplication analysis threshold

Architecture Review Cadence

Review Type	Frequency	Scope	Participants
PR-level review	Every PR	Individual changes	Developer + reviewer
Sprint-level review	End of sprint	Sprint's aggregate changes	Tech lead + senior developers
Monthly architecture review	Monthly	Cross-team patterns, dependency trends	CTO + tech leads
Quarterly architecture assessment	Quarterly	System-wide health, technical debt trends	CTO + architecture team

Measuring Architectural Health

Track these metrics to monitor the architectural impact of AI-assisted development:

Metric	Target	Warning Sign
Code duplication rate	Stable or decreasing	> 5% increase quarter-over-quarter
Cyclomatic complexity average	Stable or decreasing	Increasing trend
Dependency count growth rate	Proportional to feature growth	Growing faster than features
Dead code percentage	< 5%	> 10%
Pattern consistency score	Improving	Declining (requires custom measurement)
API surface area growth	Proportional to feature growth	Growing faster than features

info

Architecture governance is a collaborative effort. The CTO sets the direction and standards; Development Managers ensure team compliance; Developers implement within the guidelines; the QA Lead validates through testing. See the respective guides for their perspectives.

For technical risk management including dependency and vendor risks, see Technical Risk Management.