Hierarchical Multi-Agent LLM Framework For Autonomous Task Planning And Execution

Abstract

Autonomous task planning and execution using Large Language Models (LLMs) has emerged as a fundamental capability for developing intelligent systems that can solve complex, multi-step problems with minimal human intervention. Despite the promising reasoning and planning capabilities shown by recent autonomous agents implemented using LLM, existing single and flat multi-agent models tend to be poorly scaled to more complex workflows as well as belong to the category of poor task decomposition, bottlenecks in communication and reduced coordination. The paper specifies a Hierarchical Multi-agent LLM Framework to planning and execution of autonomous tasks, having the specialized LLM agents arranged into a hierarchical structure to improve the efficiency of the planning process, collaborative thought and reliability of the execution. The suggested framework comprises of Supervisor Agent, Planner Agent, Coordinator Agent, Executor Agent, Validator Agent and a Shared Memory Module which jointly execute task understanding, hierarchical decomposition, coordinated planning, autonomous execution, continuous monitoring and adaptive feedback. The methodology combines the hierarchical planning of tasks and dynamic agent coordination with shared contextual memory to effectively plan task dependencies and allow a system of powerful cooperation between many agents. The proposed framework is experimentally tested with representative autonomous task-planning benchmark datasets and compared with state-of-the-art LLM agent frameworks on Task Success Rate, Goal Completion Rate, Plan Validity, Execution Accuracy and Task Completion Time. The experimental findings show that the suggested hierarchical structure effectively enhances the quality of planning, execution effectiveness, and coordination efficiency with scalability performance in terms of increasingly complicated tasks. These results show that hierarchical multi-agent LLM systems are an effective and scalable solution to autonomous AI systems, such as software engineering, robotics, enterprise automation, and scientific reasoning.