大模型驱动的多智能体系统让任务自动分解与协作成为可能。
LLM-based Multi-Agent Systems: Techniques and Business Perspectives
- 用大模型构建可交互、调用工具的多智能体系统，实现自主任务执行。
- 相比单智能体，多智能体支持动态分工、灵活扩展和数据隐私保护。
- 适合研究集体智能、企业级AI自动化及智能体经济生态的开发者。
在（多模态）大语言模型时代，多数操作流程可重新表述并由大模型智能体实现。这些智能体能感知环境、进行控制并获取反馈，从而自主完成任务。除了环境交互能力外，大模型智能体还可调用外部工具，以辅助任务完成。这些工具可视为具有私有或实时知识的预定义操作流程，其知识并不存在于大模型参数中。作为自然发展趋势，调用工具的方式正演变为自主智能体，由此形成的完整智能系统即为基于大模型的多智能体系统（LaMAS）。相较于之前的单大模型智能体系统，LaMAS具备：i）动态任务分解与有机专业化；ii）更高的系统可变性；iii）各参与实体的数据私密性保障；iv）各实体的商业化可行性。本文探讨了LaMAS的技术与商业前景，并提出一个初步的LaMAS协议，兼顾技术需求、数据隐私与商业激励。因此，LaMAS有望成为实现近未来人工集体智能的实用方案。
原文摘要 · Abstract (English)
In the era of (multi-modal) large language models, most operational processes can be reformulated and reproduced using LLM agents. The LLM agents can perceive, control, and get feedback from the environment so as to accomplish the given tasks in an autonomous manner. Besides the environment-interaction property, the LLM agents can call various external tools to ease the task completion process. The tools can be regarded as a predefined operational process with private or real-time knowledge that does not exist in the parameters of LLMs. As a natural trend of development, the tools for calling are becoming autonomous agents, thus the full intelligent system turns out to be a LLM-based Multi-Agent System (LaMAS). Compared to the previous single-LLM-agent system, LaMAS has the advantages of i) dynamic task decomposition and organic specialization, ii) higher flexibility for system changing, iii) proprietary data preserving for each participating entity, and iv) feasibility of monetization for each entity. This paper discusses the technical and business landscapes of LaMAS. To support the ecosystem of LaMAS, we provide a preliminary version of such LaMAS protocol considering technical requirements, data privacy, and business incentives. As such, LaMAS would be a practical solution to achieve artificial collective intelligence in the near future.
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