A Comprehensive Review of Agentic AI Systems for Autonomous Reasoning, Planning, and Collaborative Decision-Making

Abstract

Another paradigm that has been created is Agentic Artificial Intelligence (AI) which goes beyond the old time generative models and moves to autonomous, goal-oriented systems which can reason, plan and collaborate in decision making. The growing complexity of the real-world application requires intelligent systems, which are able to work with limited human interventions and adapt to changing environments. This review is a systematic analysis of agentic AI systems, which examine their principles, architectural paradigms, reasoning, and planning strategies, as well as collaborative structures. A common taxonomy is created that classifies agentic systems by their operational structure, model of interaction, and the degree of autonomy. The review also gives a comparison analysis of the important architectures, such as large language model-based, cognitive, planner-executor, reinforcement learning-based, as well as hybrid ones. Moreover, various reasoning paradigms like symbolic, probabilistic, and language-based reasoning are examined and their respective advantages and disadvantages are brought out. The research provides insights into key areas of application such as software automation, robotics, medical services, financial systems, and intelligent infrastructure, proving the utility of agentic AI. Although huge advancements have been made, problems like hallucination, scaling limitations, complexity of coordination, and alignment problems still exist. The conclusion of the review to self-improving systems, trustful AI, and scalable multi-agent ecosystems presents future research directions, which gives a systematic base to the development of next-generation autonomous intelligent systems.