Artificial Intelligence (AI) has rapidly evolved from simple rule-based systems to sophisticated models capable of reasoning, learning, and acting autonomously. This book, The Rise of Agentic Intelligence Across Domains: From Theory to Real-World Applications, explores the next frontier in AI, Agentic AI, which moves beyond passive tools and predictive models to AI systems that actively make decisions, pursue goals, and interact dynamically with their environments. Unlike traditional AI, which often relies on human input for specific tasks, agentic AI exhibits autonomy, adaptability, and strategic decision-making. This paradigm shift is revolutionizing industries, from healthcare and finance to cybersecurity and smart cities, where AI agents are becoming indispensable partners in decision-making, automation, and optimization.

This book provides a comprehensive yet accessible exploration of theory, architecture, and applications of agentic AI. It begins with foundational concepts, defining agency in AI and distinguishing agentic systems from traditional automation and machine learning models. Readers will then be introduced to the core frameworks that enable AI agents to learn, adapt, and self-improve, including multi-agent systems, reinforcement learning, and neuro-symbolic AI. The discussion extends to critical ethical and safety considerations, ensuring responsible AI development and deployment. The latter half of the book focuses on real-world applications, demonstrating how AI agents are transforming industries such as healthcare, finance, industrial automation, education, and cybersecurity. Each chapter explores case studies, challenges, and future opportunities, providing readers with a well-rounded understanding of how agentic AI is shaping the future of technology and society. By the end, the book presents a forward-looking perspective on policy, regulation, and human-AI collaboration, equipping researchers, professionals, and policymakers with insights into the responsible advancement of agentic AI.