LLM-based Multi-Agent Systems: Techniques and Business Perspectives
This work addresses the problem of scaling and optimizing operational processes for businesses and AI developers by introducing a multi-agent framework, though it appears incremental as an extension of existing single-agent systems.
The paper tackles the challenge of creating intelligent systems by proposing LLM-based Multi-Agent Systems (LaMAS), which use multiple autonomous agents to dynamically decompose tasks and integrate tools, offering advantages like flexibility and data privacy. It presents a preliminary protocol to support LaMAS, aiming to achieve artificial collective intelligence.
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.