Bridging the Prototype-Production Gap: A Multi-Agent System for Notebooks Transformation

The increasing adoption of Jupyter notebooks in data science and machine learning workflows has created a gap between exploratory code development and production-ready software systems. While notebooks excel at iterative development and visualization, they often lack proper software engineering principles, making their transition to production environments challenging. This paper presents Codelevate<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>, a novel multiagent system that automatically transforms Jupyter notebooks into well-structured, maintainable Python code repositories. Our system employs three specialized agents - Architect, Developer, and Structure - working in concert through a shared dependency tree to ensure architectural coherence and code quality. Our experimental results validate Codelevate’s capability to bridge the prototype-to-production gap through autonomous code transformation, yielding quantifiable improvements in code quality metrics while preserving computational semantics.<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>Source Code: https://github.com/siemens-research/genai-for-sw-architecture.