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arXiv · 2410.22129

Improving Performance of Commercially Available AI Products in a Multi-Agent Configuration

Cory Hymel·Sida Peng·Kevin Xu·Charath Ranganathan
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Abstract

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.

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Cory Hymel, Sida Peng, Kevin Xu, Charath Ranganathan. 2024-10-29. Improving Performance of Commercially Available AI Products in a Multi-Agent Configuration. https://arxiv.org/abs/2410.22129

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