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This paper introduces a Hierarchical Agentic AI Framework that enhances autonomous task planning by employing a multi-agent architecture, which includes Supervisor, Planning, and specialized execution agents. The framework effectively analyzes user goals, decomposes them into manageable subtasks, and coordinates their execution through reasoning-driven workflows, leveraging chain-of-thought reasoning and contextual memory. Experimental results indicate significant improvements in task completion accuracy, planning efficiency, and adaptability compared to traditional single-agent and flat multi-agent systems, highlighting the framework's potential for scalable solutions in enterprise automation and decision support.
Hierarchical agentic architectures can drastically improve task planning and execution efficiency, outperforming traditional models in dynamic environments.
Large Language Models (LLMs) have brought major improvements in natural language understanding, reasoning, and intelligent content generation. Although these models perform well in many applications, most existing AI systems still face difficulties while handling complex multi-step tasks that require planning, coordination, and adaptability. The standard single-agent systems do not work for tasks with multiple dependencies, changing objectives, or long execution workflows. Hierarchical Agentic AI Framework For Autonomous Task Planning With Large Language Model Reasoning This paper The proposed framework is structured as a hierarchical multi-agent architecture comprising Supervisor Agent, Planning Agent, specialized execution agents and validation layer. The framework aims at analyzing user goals, breaking them down into smaller executable subtasks, and coordinating the execution of the tasks using reasoning-driven workflows. The system combines chain-of-thought reasoning, contextual memory and intelligent task decomposition to improve the quality of planning and the accuracy of execution. Experimental evaluation demonstrates the superiority of the proposed framework in task completion accuracy, planning efficiency and adaptability, compared to traditional single agent and flat multi-agent architectures. Moreover, the framework shows improved coordination and reduced redundant execution in dynamic workflow settings. The results show that hierarchical agentic architectures can deliver scalable and intelligent solutions for autonomous workflow execution in enterprise automation, research assistance and AI-driven decision support systems.