A Comprehensive Review of Agentic AI for Intelligent Autonomous Systems
Keywords:
Agentic Artificial Intelligence, Intelligent Agents, Autonomous Systems, Large Language Models, Multi-Agent SystemsAbstract
Artificial Intelligence (AI) has advanced from traditional rule-based and data-driven machine learning models to intelligent autonomous systems that can reason, plan, learn and perform complicated tasks with little human interaction. This paradigm change has resulted in the advent of Agentic Artificial Intelligence, combining autonomous decision-making, memory, goal-oriented planning, tool use, multi-agent cooperation, and continuous adaptation into a single intelligent framework. This paper provides a detailed discussion of the core principles, architectural elements, enabling technologies, applications, problems and future research directions of Agentic AI. In the first part of the study, the historical history of intelligent agents is traced, starting with symbolic AI, expert systems, multi-agent systems, reinforcement learning and cognitive architectures to the present massive language model driven autonomous agents. And then, the review offers a systematic taxonomy on perception, reasoning and memory management, planning mechanisms, tool integration, collaborative multi-agent coordination, adaptive learning, explainability and governance. Finally, the article discusses future research prospects that are predicted to expedite the development of robust, explainable and trustworthy Agentic AI capable of enabling sophisticated autonomous decision making across heterogeneous intelligent systems.
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