Architectural Constraints Alignment in AI-assisted, Platform-based Service Development
Julius Irion, Moritz Leugers, Paul Hartwig, Simon Kling, Tachmyrat Annayev + 3 more
TLDR
A retrieval-augmented scaffolding approach aligns AI-assisted service development with architectural constraints, improving production readiness and deployability.
Key contributions
- Addresses AI-assisted development's lack of architectural constraint awareness.
- Introduces retrieval-augmented scaffolding for production-ready service generation.
- Uses platform-based code generation combined with agentic clarification loops.
- Improves architectural consistency and deployability over general AI code.
Why it matters
AI-assisted code generation often produces non-production-ready code due to ignored architectural constraints. This paper offers a practical solution to align AI-generated services with enterprise standards, ensuring robust and deployable outcomes for real-world software engineering.
Original 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.
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