Architectural Constraints Alignment in AI-assisted, Platform-based Service Development
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.
Cite
@article{arxiv.2605.04973,
title = {Architectural Constraints Alignment in AI-assisted, Platform-based Service Development},
author = {Julius Irion and Moritz Leugers and Paul Hartwig and Simon Kling and Tachmyrat Annayev and Alexander Schwind and Maria C. Borges and Sebastian Werner},
journal= {arXiv preprint arXiv:2605.04973},
year = {2026}
}
Comments
To Appear at CAiSE'26 - LLM-SOA Workshop