Architectural Constraints Alignment in AI-assisted, Platform-based Service Development
For developers using AI-assisted tools in production environments, this work addresses the critical gap between rapid prototyping and deployable software by embedding architectural constraints into the generation process.
The paper tackles the problem of AI-assisted development tools generating code that lacks awareness of architectural constraints, leading to brittle and non-deployable artifacts. They propose a retrieval-augmented scaffolding approach with agentic clarification loops, achieving improved architectural consistency and deployability compared to general-purpose AI code generation.
AI-assisted development tools enable rapid prototyping of services but often lack awareness of architectural constraints, infrastructure dependencies, and organizational standards required in production environments. Consequently, generated artifacts may exhibit brittle behavior and limited deployability. We propose a retrieval-augmented scaffolding approach that combines platform-based code generation with agentic clarification loops to expose and resolve architectural constraint ambiguities. By combining template retrieval with structured interaction, the method embeds production-relevant considerations during service scaffolding. Evaluation indicates improved architectural consistency and deployability compared to general-purpose AI code generation workflows, suggesting that constraint-aware retrieval is essential for aligning AI-assisted service development with production software engineering practices.