Towards Intelligent and Scalable Software Documentation Practices via with Multi-Agent LLMs

Multi-agent Large Language Models (LLMs) represent a promising direction for advancing software documentation, addressing the shortcomings of traditional approaches that often fail to scale with the complexity of modern codebases. By deploying multiple specialized agents-each powered by an LLM—these systems collaboratively analyze code at different granularities, from individual files and modules to entire software projects. This multi-agent paradigm enables a holistic understanding of code, moving beyond isolated snippet-level descriptions to capture the broader architectural patterns, interdependencies, and contextual nuances essential for comprehensive documentation.

However, several challenges remain. Ensuring the accuracy and consistency of generated documentation, especially in highly specialized or domain-specific codebases, remains a difficult task. Additionally, the computational demands of coordinating multiple agents can be significant. Effective communication and synchronization among agents are critical to maintaining coherence across documentation layers and abstraction levels. These are some of the elements our research currently focuses on.

Looking ahead, our research aims to push the boundaries of multi-agent LLM systems by enhancing their reasoning capabilities for deeper and more context-aware code understanding, developing robust coordination strategies to ensure coherent agent collaboration, and integrating these solutions seamlessly into practical software engineering workflows. By tackling these core challenges, our overarching goal is the development of intelligent documentation systems that can scale across projects, adapt to diverse domains, and support developers throughout the software lifecycle. Ultimately, we envision multi-agent LLMs as a transformative force in software documentation – promoting code transparency, maintainability, and accessibility.