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 multi-LLM-agent system (MLAS). This paper discusses the technical and business landscapes of MLAS. Compared to the previous single-LLM-agent system, a MLAS has the advantages of i) higher potential of task-solving performance, ii) higher flexibility for system changing, iii) proprietary data preserving for each participating entity, and iv) feasibility of monetization for each entity. To support the ecosystem of MLAS, we provide a preliminary version of such MLAS protocol considering technical requirements, data privacy, and business incentives. As such, MLAS would be a practical solution to achieve artificial collective intelligence in the near future.

翻译：在（多模态）大语言模型时代，大多数操作流程都可以通过LLM智能体进行重构与复现。LLM智能体能够感知环境、控制环境并获取环境反馈，从而以自主方式完成指定任务。除环境交互特性外，LLM智能体还能调用各类外部工具以简化任务执行过程。这些工具可视为预定义的操作流程，其包含LLM参数中不存在的私有知识或实时知识。随着技术发展的自然趋势，被调用工具正逐渐演变为自主智能体，从而使完整智能系统发展为多LLM智能体系统（MLAS）。本文探讨MLAS的技术格局与商业前景。相较于先前的单LLM智能体系统，MLAS具有以下优势：i) 任务解决性能潜力更高，ii) 系统变更灵活性更强，iii) 各参与实体的专有数据得以保留，iv) 各实体具备货币化可行性。为支持MLAS生态系统，我们提出了兼顾技术要求、数据隐私与商业激励的MLAS协议初步版本。由此，MLAS将成为在近期实现人工集体智能的可行解决方案。