This chapter introduces the conceptual motivation behind Agentic Artificial Intelligence (AI) by tracing the historical evolution of AI paradigms, from rule-based systems to data-driven machine learning and multi-agent approaches. It highlights the limitations of classical AI systems in addressing complex, open-ended, and dynamic real-world problems, particularly those requiring long-term autonomy, contextual reasoning, and adaptive decision-making. By framing intelligence as an emergent, situated, and goal-directed process, the chapter establishes agentivity as a central organising principle. The discussion situates Agentic AI as a response to the growing need for systems capable of integrating perception, learning, reasoning, and interaction over extended time horizons. It concludes by outlining the book’s structure and positioning Agentic AI as a unifying paradigm that bridges symbolic reasoning, learning-based methods, and autonomous multi-agent coordination.