Architectural Constraints Alignment in AI-assisted, Platform-based Service Development

TL;DR

This paper presents a retrieval-augmented scaffolding method that improves architectural consistency and deployability in AI-assisted service development by integrating constraint-awareness into code generation.

Contribution

It introduces a novel approach combining template retrieval and interaction loops to embed production constraints during AI-driven service scaffolding.

Findings

Enhanced architectural consistency in generated services

Improved deployability over standard AI code generation workflows

Constraint-aware retrieval aligns AI development with production practices

Abstract

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.