LLM-based Multi-Agent Systems: Techniques and Business Perspectives

TL;DR

This paper explores the development of LLM-based Multi-Agent Systems (LaMAS), highlighting their technical architecture, advantages over single-agent systems, and potential business applications and incentives.

Contribution

It introduces a preliminary LaMAS protocol addressing technical, privacy, and business considerations, advancing the practical deployment of collective AI systems.

Findings

LaMAS enables dynamic task decomposition and specialization.

LaMAS offers higher system flexibility and data privacy.

Proposed protocol supports technical and business ecosystem development.

Abstract

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.