LLM-Guided Multi-Agent Collaboration for Complex Task Automation

The combination of multi-agent systems and Large Language Models (LLMs) has become an exciting new paradigm capable of the real-time automation of complex tasks. In this paper, we introduce a framework of LLM-Guided Multi-Agent Collaboration that utilizes LLMs as high-level reasoning engines to coordinate, optimize and adjust the interactions between multiple autonomous agents. The proposed framework builds on almost zero task decomposition and intent interpretation in multitasking and resolution of conflict that is provided by the natural language understanding and decision support capability of the LLMs. Agents that have domain-specific knowledge will work in cooperation, under LLM supervision to deliver an agent-centric task execution that is scalable, explainable and adaptive. We consider the framework against the disparate domains of process automation, data-driven decision-making, and cybersecurity incident response. In the experimental results, better efficiency, resource use, and flexibility were observed in respect to dedicated multi-agent models. This paper indicates the promise that LLM-based coordination may hold to turn multi-agent collaboration into a more intelligent, context-aware, and robust paradigm of automating complex tasks in the real world.