Summary of Towards Effective Genai Multi-agent Collaboration: Design and Evaluation For Enterprise Applications, by Raphael Shu et al.
Towards Effective GenAI Multi-Agent Collaboration: Design and Evaluation for Enterprise Applications
by Raphael Shu, Nilaksh Das, Michelle Yuan, Monica Sunkara, Yi Zhang
First submitted to arxiv on: 6 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel multi-agent collaboration framework is presented in this report, which combines multiple large language models (LLMs) to tackle complex problems that exceed the capabilities of single AI agents. The framework is designed to enable coordination and routing between agents, enabling effective parallel communication and payload referencing. This medium-level technical summary focuses on the framework’s operational modes, including a coordination mode for complex task completion and a routing mode for efficient message forwarding. Benchmarking is performed on handcrafted scenarios from three enterprise domains, demonstrating the effectiveness of inter-agent communication and payload referencing mechanisms in achieving end-to-end goal success rates of 90%. Key findings include a 70% increase in goal success rates with multi-agent collaboration compared to single-agent approaches, as well as improvements in code-intensive tasks by 23% through payload referencing. The report offers valuable guidance for enterprise deployments and advances the development of scalable, efficient multi-agent collaboration frameworks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper presents a way to combine many AI agents to solve complex problems that are too big for one agent alone. The agents work together using special protocols, called coordination and routing modes. These protocols let the agents communicate and share information in parallel, which helps them complete tasks faster and more accurately. The authors tested this approach on scenarios from three different industries, such as finance or healthcare, and found that it works really well. They also discovered that when the agents work together, they can solve problems up to 70% better than if each agent worked alone. This is important because many real-world problems require a team effort to solve. |
