Architectures for Agentic AI: Integrating Multi-Agent Systems, Reinforcement Learning, and LLMs for Autonomous Decision-Making (SpringerBriefs in Intelligent Systems)

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