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

TL;DR

This paper evaluates a novel multi-agent collaboration framework powered by large language models, demonstrating significant improvements in goal success rates and efficiency for enterprise applications through coordination and routing mechanisms.

Contribution

It introduces a comprehensive evaluation of coordination and routing modes in multi-agent systems, providing benchmarks and insights for enterprise deployment.

Findings

Multi-agent collaboration increases goal success by up to 70%.

Payload referencing boosts code task performance by 23%.

Routing reduces latency by bypassing agent orchestration.

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.