Architectural Constraints Alignment in AI-assisted, Platform-based Service Development
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arXiv
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| Auteurs principaux: | , , , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866913094868926464 |
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| author | Irion, Julius Leugers, Moritz Hartwig, Paul Kling, Simon Annayev, Tachmyrat Schwind, Alexander Borges, Maria C. Werner, Sebastian |
| author_facet | Irion, Julius Leugers, Moritz Hartwig, Paul Kling, Simon Annayev, Tachmyrat Schwind, Alexander Borges, Maria C. Werner, Sebastian |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_04973 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Architectural Constraints Alignment in AI-assisted, Platform-based Service Development Irion, Julius Leugers, Moritz Hartwig, Paul Kling, Simon Annayev, Tachmyrat Schwind, Alexander Borges, Maria C. Werner, Sebastian Software Engineering Artificial Intelligence 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. |
| title | Architectural Constraints Alignment in AI-assisted, Platform-based Service Development |
| topic | Software Engineering Artificial Intelligence |
| url | https://arxiv.org/abs/2605.04973 |