A Multi-Agent LLM Environment for Software Design and Refactoring: A Conceptual Framework

Modern software systems demand continuous evolution to maintain performance, scalability, and security. Traditional single-agent AI-driven code refactoring approaches are often limited in addressing the multi-faceted constraints (e.g., performance, security, maintainability) that emerge during complex software design tasks. In this paper, we propose a novel Multi-Agent Large Language Model (LLM) Environment for automated software design and refactoring. Our conceptual framework comprises specialized LLM “experts,” each trained or fine-tuned on a different aspect of software engineering (performance optimization, security hardening, UI/UX, maintainability). These agents collaborate in a cooperative or competitive fashion-using coordination protocols akin to consensus or auction mechanisms-to synthesize design insights and refactoring recommendations. We present formal definitions of agent interactions (including mathematical notation for termination conditions), a sequence diagram demonstrating agent collaboration, a complexity analysis of the coordination mechanism, and an expanded reference list. Preliminary experimental design is outlined to demonstrate how multi-agent interactions may resolve conflicting design goals more effectively than a single-agent approach. Our aim is to provide a roadmap for integrating multi-agent LLMs into the software development lifecycle, thereby improving development efficiency, reducing technical debt, and enhancing software quality.