In recent years, with the rapid advancement of large language models (LLMs), multi-agent systems have become increasingly more capable of practical application. At the same time, the software development industry has had a number of new AI-powered tools developed that improve the software development lifecycle (SDLC). Academically, much attention has been paid to the role of multi-agent systems to the SDLC. And, while single-agent systems have frequently been examined in real-world applications, we have seen comparatively few real-world examples of publicly available commercial tools working together in a multi-agent system with measurable improvements. In this experiment we test context sharing between Crowdbotics PRD AI, a tool for generating software requirements using AI, and GitHub Copilot, an AI pair-programming tool. By sharing business requirements from PRD AI, we improve the code suggestion capabilities of GitHub Copilot by 13.8% and developer task success rate by 24.5% -- demonstrating a real-world example of commercially-available AI systems working together with improved outcomes.

翻译：近年来，随着大语言模型（LLMs）的快速发展，多智能体系统在实际应用中的能力日益增强。与此同时，软件开发行业已涌现出众多新型AI驱动工具，旨在改善软件开发生命周期（SDLC）。学术界对多智能体系统在SDLC中的作用给予了大量关注。然而，尽管单智能体系统在现实应用中的研究已较为常见，但公开可用的商业工具在多智能体系统中协同工作并带来可量化性能提升的实际案例相对较少。本实验测试了Crowdbotics PRD AI（一款利用AI生成软件需求的工具）与GitHub Copilot（一款AI结对编程工具）之间的上下文共享。通过共享PRD AI生成的业务需求，我们将GitHub Copilot的代码建议能力提升了13.8%，并将开发者的任务成功率提高了24.5%——这为商用AI系统协同工作并实现改进成果提供了一个现实案例。