Towards Effective GenAI Multi-Agent Collaboration: Design and Evaluation for Enterprise Applications

• URL: http://arxiv.org/abs/2412.05449v1
• Date: Fri, 06 Dec 2024 22:14:17 GMT
• Title: Towards Effective GenAI Multi-Agent Collaboration: Design and Evaluation for Enterprise Applications
• Authors: Raphael Shu, Nilaksh Das, Michelle Yuan, Monica Sunkara, Yi Zhang,
• Abstract summary: This report presents a comprehensive evaluation of coordination and routing capabilities in a novel multi-agent collaboration framework.<n>For coordination capabilities, we demonstrate the effectiveness of inter-agent communication and payload referencing mechanisms, achieving end-to-end goal success rates of 90%.<n>Our analysis yields several key findings: multi-agent collaboration enhances goal success rates by up to 70% compared to single-agent approaches in our benchmarks.
• Score: 15.480315462362531
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: AI agents powered by large language models (LLMs) have shown strong capabilities in problem solving. Through combining many intelligent agents, multi-agent collaboration has emerged as a promising approach to tackle complex, multi-faceted problems that exceed the capabilities of single AI agents. However, designing the collaboration protocols and evaluating the effectiveness of these systems remains a significant challenge, especially for enterprise applications. This report addresses these challenges by presenting a comprehensive evaluation of coordination and routing capabilities in a novel multi-agent collaboration framework. We evaluate two key operational modes: (1) a coordination mode enabling complex task completion through parallel communication and payload referencing, and (2) a routing mode for efficient message forwarding between agents. We benchmark on a set of handcrafted scenarios from three enterprise domains, which are publicly released with the report. For coordination capabilities, we demonstrate the effectiveness of inter-agent communication and payload referencing mechanisms, achieving end-to-end goal success rates of 90%. Our analysis yields several key findings: multi-agent collaboration enhances goal success rates by up to 70% compared to single-agent approaches in our benchmarks; payload referencing improves performance on code-intensive tasks by 23%; latency can be substantially reduced with a routing mechanism that selectively bypasses agent orchestration. These findings offer valuable guidance for enterprise deployments of multi-agent systems and advance the development of scalable, efficient multi-agent collaboration frameworks.

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