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

2024-11-21

AI Summary

Large language model-based multi-agent systems (LaMAS) face inherent tensions among task decomposition, system flexibility, data privacy, and commercial viability. Method: This paper proposes the first technology–business co-designed LaMAS protocol framework, integrating dynamic task decomposition, tool-augmented reasoning, privacy-aware collaborative scheduling, and entity-level data sovereignty preservation, alongside lightweight service-oriented interfaces and a standardized multi-agent communication protocol. Contributions/Results: (1) It introduces an organic specialization paradigm coupled with adaptive system evolution; (2) it simultaneously ensures private data isolation and alignment with multi-stakeholder commercial incentives; and (3) it establishes the first infrastructure for scalable LaMAS deployment. Empirical evaluation demonstrates significant improvements in task adaptability, compliance with GDPR and China’s Personal Information Protection Law, and commercial feasibility—laying a foundation for scalable, governable, and sustainable artificial collective intelligence.

Technology Category

• Multiagent Systems: Mechanism Design
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Safety and Robustness

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Security and Privacy: Data transparency and provenance
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

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.

Problem

• Large Language Models
• Multi-Agent Systems
• Privacy and Business Model Balance

Innovation

• LaMAS System
• Multi-Agent Collaboration
• Large Language Model Integration