AutoGenesisAgent: Self-Generating Multi-Agent Systems for Complex Tasks

TL;DR

AutoGenesisAgent is an innovative system that autonomously designs, generates, and deploys multi-agent systems for complex tasks, leveraging large language models to reduce human intervention in system development.

Contribution

It introduces a novel autonomous multi-agent system that manages the entire lifecycle of creating and deploying other multi-agent systems using LLMs.

Findings

Successfully demonstrated autonomous system generation and deployment.

Reduced human oversight in multi-agent system design.

Showed effective optimization of system performance.

Abstract

The proliferation of large language models (LLMs) and their integration into multi-agent systems has paved the way for sophisticated automation in various domains. This paper introduces AutoGenesisAgent, a multi-agent system that autonomously designs and deploys other multi-agent systems tailored for specific tasks. AutoGenesisAgent comprises several specialized agents including System Understanding, System Design, Agent Generator, and several others that collectively manage the lifecycle of creating functional multi-agent systems from initial concept to deployment. Each agent in AutoGenesisAgent has distinct responsibilities ranging from interpreting input prompts to optimizing system performance, culminating, in the deployment of a ready-to-use system. This proof-of-concept study discusses the design, implementation, and lessons learned from developing AutoGenesisAgent, highlighting its capability to generate and refine multi-agent systems autonomously, thereby reducing the need for extensive human oversight in the initial stages of system design. Keywords: multi-agent systems, large language models, system design automation, agent architecture, autonomous systems, software deployment