What this book covers
Chapter 1, Fundamentals of Generative AI, introduces generative AI, explaining its core concepts, various model types—including VAEs, GANs, and autoregressive models—real-world applications, and challenges such as bias, limitations, and ethical concerns.
Chapter 2, Principles of Agentic Systems, defines agentic systems, covering agency, autonomy, and the essential characteristics of intelligent agents, including reactivity, proactiveness, and social ability. It also explores different agent architectures and multi-agent collaboration.
Chapter 3, Essential Components of Intelligent Agents, details key elements of intelligent agents, including knowledge representation, reasoning, learning mechanisms, decision-making, and the role of Generative AI in enhancing agent capabilities.
Chapter 4, Reflection and Introspection in Agents, explores how intelligent agents analyze their reasoning, learn from experience, and improve decision-making using techniques such as meta-reasoning, self-explanation, and self-modeling.
Chapter 5, Enabling Tool Use and Planning in Agents, discusses how agents leverage external tools, implement planning algorithms, and integrate tool use with strategic decision-making to improve efficiency and goal achievement.
Chapter 6, Exploring the Coordinator, Worker, and Delegator Approach, introduces the CWD model for multi-agent collaboration, explaining how agents take on specialized roles—coordinator, worker, or delegator—to optimize task execution and resource allocation.
Chapter 7, Effective Agentic System Design Techniques, covers best practices for designing intelligent agents, including focused instructions, setting guardrails and constraints, balancing autonomy and control, and ensuring transparency and accountability.
Chapter 8, Building Trust in Generative AI Systems, examines techniques for fostering trust in AI, including transparency, explainability, handling uncertainty and bias, and designing AI systems that are reliable and interpretable.
Chapter 9, Managing Safety and Ethical Considerations, addresses the risks and challenges of generative AI, strategies for ensuring responsible AI development, ethical guidelines, and privacy and security considerations for AI deployments.
Chapter 10, Common Use Cases and Applications, showcases real-world applications of Generative AI, covering areas such as creative content generation, conversational AI, robotics, and decision-support systems.
Chapter 11, Conclusion and Future Outlook, summarizes key concepts covered in the book, explores emerging trends in generative AI and agentic intelligence, discusses artificial general intelligence (AGI), and highlights future challenges and opportunities in the field.
