AutoGenesisAgent: Self-Generating Multi-Agent Systems for Complex Tasks
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arXiv
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866916224110166016 |
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| author | Harper, Jeremy |
| author_facet | Harper, Jeremy |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_17017 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | AutoGenesisAgent: Self-Generating Multi-Agent Systems for Complex Tasks Harper, Jeremy Multiagent Systems 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 |
| title | AutoGenesisAgent: Self-Generating Multi-Agent Systems for Complex Tasks |
| topic | Multiagent Systems |
| url | https://arxiv.org/abs/2404.17017 |