Multi-Agent LLMs for Autonomous Workflow Orchestration
DOI:
https://doi.org/10.15662/IJRAI.2025.0801002Keywords:
Multi-Agent Systems, Large Language Models (LLMs), Workflow Orchestration, Autonomous Systems, Task Planning, Agent Communication, Natural Language Interfaces, Prompt Engineering, Distributed AI, Self-Healing SystemsAbstract
Workflow orchestration plays a pivotal role in modern computing systems, automating complex, multistep tasks across distributed services and agents. Traditional orchestration frameworks are typically rule-based, brittle, and require explicit programming for every scenario. With the advancement of Large Language Models (LLMs) and multi-agent systems, there emerges an opportunity to develop autonomous, adaptive workflow orchestration mechanisms capable of reasoning, collaboration, and dynamic task allocation. This paper investigates a novel approach leveraging Multi-Agent LLMs for autonomous workflow orchestration in complex environments such as cloud computing, robotic systems, and enterprise automation. Each agent in the system is powered by a specialized or generalized LLM that can interpret natural language instructions, generate subtasks, communicate with other agents, and execute or delegate actions based on context. The proposed system architecture features agents with roles such as "Planner", "Executor", and "Monitor", which interact using a shared language protocol. These agents collectively manage workflows by breaking down high-level goals into actionable steps, reallocating tasks in real-time, and recovering from failures without human intervention. We evaluate our multi-agent orchestration system in both simulated environments (e.g., software pipelines) and realworld scenarios (e.g., document processing, robotic task planning). Results show that LLM-powered agents improve task completion rates, reduce latency, and handle unanticipated failures more effectively than traditional orchestrators. The methodology combines prompt engineering, role assignment, context sharing, and chain-of-thought reasoning across agents. Challenges include prompt drift, coordination failure, and high computational cost. This paper contributes a framework, performance evaluation, and a discussion of trade-offs, emphasizing the feasibility and advantages of using LLMs in distributed multi-agent environments. The conclusion outlines future directions, including reinforcement learning for coordination and integrating symbolic reasoning for verifiability.
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