The book begins by defining the core principles of Agentic AI, highlighting its distinction from conventional machine learning systems. It delves into key components such as cognitive architectures, decision-making frameworks, multi-agent collaboration, reinforcement learning, and memory management, explaining how AI agents perceive, reason, and adapt. Advanced techniques like hybrid AI architectures, self-optimization loops, and meta-learning are discussed in depth, demonstrating how AI agents continuously refine their capabilities.
Practical applications of Agentic AI are explored in healthcare, autonomous vehicles, finance, business automation, and multi-agent systems. The book examines real-world challenges, including AI alignment, security threats, ethical dilemmas, and regulatory compliance, ensuring responsible deployment of self-improving AI.
Finally, the book looks ahead to the future of AI, covering advancements toward Artificial General Intelligence (AGI), the role of explainable AI (XAI), and the emerging trends shaping next-generation AI systems.
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