This book explores the emerging paradigm of Agentic AI, where Large Language Models (LLMs) and Reinforcement Learning (RL) converge to create intelligent, autonomous, and adaptive systems. It provides a unified theoretical foundation and connects it to practical implementation, offering readers a clear path from concept to execution. It will also provide an integrative approach of Agentic AI, Large Language Models, and Reinforcement Learning. While these topics are often studied separately, this book provides a coherent framework that unites them, filling a critical gap between AI theory, system design, and real-world application. In an era of rapidly evolving AI technologies, understanding how Agentic AI systems operate, and how they differ from traditional AI, is essential. This book guides researchers, engineers, and AI practitioners through the architectural principles that empower agents to reason, cooperate, and learn from feedback. It further demonstrates how RL can fine-tune LLMs to produce more focused, context-aware outputs, strengthening their role in multi-agent collaboration and autonomous decision-making. The content unfolds from the evolution of AI to Agentic AI,…

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