Agentic AI: The age of reasoning—A review

Artificial intelligence has experienced a significant boom with the emergence of agentic AI, where autonomous agents are increasingly replacing human intervention, enabling systems to perceive, reason, and act independently to achieve specific goals. Despite its transformative potential, comprehensive information on agentic AI remains scarce in the literature. This paper provides the first comprehensive review of agentic AI, focusing on its evolution and three core aspects: patterns, types, and environments. The evolution of agentic AI is traced through five phases to the current era of multi-modal and collaborative agents, driven by advancements in reinforcement learning, neural networks, and large language models (LLMs). Five key patterns: tool use, reflection, ReAct, planning, and multi-agent collaboration (MAC) define how agentic AI systems interact and process tasks. These systems are categorized into seven categories, each tailored for specific operational styles and autonomy in decision making. The environments in which these agents operate are classified as static, dynamic, fully observable, partially observable, deterministic, stochastic, single-agent, and multi-agent, emphasizing the impact of environmental complexity on agent behavior. Agentic AI has revolutionized systems through autonomous decision making and resource optimization, yet challenges persist in aligning AI with human values, ensuring adaptability, and addressing ethical constraints. Future research focuses on multidomain agents, human–AI collaboration, and self-improving systems. This work provides researchers, practitioners, and policymakers with a structured approach to understanding and advancing the rapidly evolving landscape of agentic AI systems. • Trace agentic AI’s growth across five phases from 1980s to present. • Analyze taxonomy of seven agent types and five operational design patterns. • Evaluate agentic AI behavior in different environments. • Identify gaps in alignment, ethics, and scalability and recommend future paths.