From Generative Intelligence to Agentic Autonomy: Leveraging Large Language Models for Multi-Agent Reasoning, Planning, and Execution
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Abstract
The rapid advancement of Large Language Models (LLMs) has transformed artificial intelligence from systems primarily focused on content generation to autonomous agents capable of reasoning, planning, and executing complex tasks. This evolution has given rise to Agentic AI, where intelligent agents operate independently, collaborate with other agents, and interact with external tools and environments to achieve defined objectives. This paper explores the transition from generative intelligence to agentic autonomy, examining how LLMs serve as the cognitive foundation for multi-agent systems. The study investigates key architectural components including memory management, task decomposition, planning mechanisms, tool integration, and inter-agent communication frameworks. Furthermore, it analyzes contemporary approaches such as retrieval-augmented generation (RAG), reinforcement learning, orchestration frameworks, and autonomous decision-making processes that enable coordinated problem-solving across distributed agent ecosystems. Through an examination of emerging applications in enterprise automation, scientific research, financial services, healthcare, and software engineering, the paper highlights the opportunities and challenges associated with deploying agentic systems at scale. Critical issues related to trustworthiness, explainability, governance, security, and ethical decision-making are also discussed. The findings suggest that multi-agent LLM architectures represent a significant step toward autonomous digital ecosystems capable of adaptive reasoning and collaborative execution, paving the way for the next generation of intelligent systems that can operate with minimal human intervention while maintaining alignment with organizational and societal objectives
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