Agentic Artificial Intelligence (Agentic AI) refers to AI systems that can plan, take actions and adapt their behavior to achieve goals with limited human supervision. This review traces the evolution of agentic AI from classical intelligent agents and multi-agent systems to today’s Large Language Model (LLM)-based agents that use tools, memory and multi-step reasoning. The chapter synthesizes key ideas, architectures, enabling technologies, evaluation methods, real-world applications and emerging safety and governance practices. It highlights how modern agentic systems combine planning, tool use, retrieval, reflection and coordination across multiple agents, while also introducing new risks such as unsafe autonomy, prompt injection and reliability failures. Finally, the review proposes research directions toward more robust, transparent and accountable agentic AI suitable for high-stakes deployment.

Agentic AI has evolved from classical intelligent agent concepts and multi-agent coordination into modern LLM-based systems capable of planning, tool use, memory, reflection and multi-agent collaboration. Research from ReAct, Toolformer, Reflexion, Tree of Thoughts and major frameworks such as AutoGen, SWE-agent and Voyager shows rapid progress in practical autonomy. At the same time, agentic AI amplifies safety and governance concerns because it can take actions and affect real systems. Future progress toward publishable, real-world-grade agentic AI will depend on stronger evaluation, robust grounding, principled memory, secure tool interfaces and alignment with risk management and regulatory frameworks.

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