Using Apex frameworks to enforce code quality and platform limits in the AI era
The author proposes Framework-Driven Development as a response to the risks of AI-assisted coding on the Salesforce platform. Because large language models generate probabilistic output that often ignores existing architecture, developers risk introducing inefficient code that consumes shared governor limits. The approach shifts responsibility for code quality and performance from individual engineers to the underlying framework itself.
Framework-Driven Development relies on three main assumptions. Interfaces must be deliberately restricted so that neither developers nor AI agents can bypass established rules. Internal logic should automatically optimize queries and data operations to respect platform boundaries. Finally, quality must be baked into the design through compile-time contracts and runtime validation that force explicit overrides when defaults are intentionally ignored.
Real-world implementations demonstrate these principles through specialized libraries. A trigger handler restricts available methods based on execution context, preventing invalid database operations before compilation. A query builder enforces caching rules and blocks complex nested conditions that could return misleading results. A data manipulation layer chains operations safely while managing sharing modes and commit strategies. Each component throws descriptive exceptions when misused, requiring deliberate action to bypass safeguards.
The methodology also emphasizes comprehensive documentation and structured prompts to guide artificial intelligence tools. By exposing only safe public methods and providing clear usage examples, teams can maintain consistent standards across multiple projects. This reduces technical debt and ensures that automated code generation aligns with enterprise architecture requirements.