Building Agentic AI Systems: Create intelligent, autonomous AI agents that can reason, plan, and adapt

Description

Master the art of building AI agents with large language models using the coordinator, worker, and delegator approach for orchestrating complex AI systems Key Features Understand the foundations and advanced techniques of building intelligent, autonomous AI agents Learn advanced techniques for reflection, introspection, tool use, planning, and collaboration in agentic systems Explore crucial aspects of trust, safety, and ethics in AI agent development and applications Purchase of the print or Kindle book includes a free PDF eBook Book Description Gain unparalleled insights into the future of AI autonomy with this comprehensive guide to designing and deploying autonomous AI agents that leverage generative AI (GenAI) to plan, reason, and act. Written by industry-leading AI architects and recognized experts shaping global AI standards and building real-world enterprise AI solutions, it explores the fundamentals of agentic systems, detailing how AI agents operate independently, make decisions, and leverage tools to accomplish complex tasks. Starting with the foundations of GenAI and agentic architectures, you’ll explore decision-making frameworks, self-improvement mechanisms, and adaptability. The book covers advanced design techniques, such as multi-step planning, tool integration, and the coordinator, worker, and delegator approach for scalable AI agents. Beyond design, it addresses critical aspects of trust, safety, and ethics, ensuring AI systems align with human values and operate transparently. Real-world applications illustrate how agentic AI transforms industries such as automation, finance, and healthcare. With deep insights into AI frameworks, prompt engineering, and multi-agent collaboration, this book equips you to build next-generation adaptive, scalable AI agents that go beyond simple task execution and act with minimal human intervention. What you will learn Master the core principles of GenAI and agentic systems Understand how AI agents operate, Cover Title page Copyright Foreword Foreword 2 Contributors Table of Contents Preface Part 1: Foundations of Generative AI and Agentic Systems Chapter 1: Fundamentals of Generative AI Introduction to generative AI Types of generative AI models VAEs GANs Autoregressive models and Transformer architecture LLM-powered AI agents Applications of generative AI Challenges and limitations of generative AI Quality of data and bias Data privacy Computational resources Ethical and social implications Generalization and creativity Summary Questions Answers Further reading References Chapter 2: Principles of Agentic Systems Technical requirements Understanding self-governance, agency, and autonomy Self-governance Agency Autonomy Example of agency and autonomy in agents Reviewing intelligent agents and their characteristics Exploring the architecture of agentic systems Deliberative architectures Reactive architectures Hybrid architectures Understanding multi-agent systems Definition and characteristics of MASs Interaction mechanisms in MASs Summary Questions Answers Chapter 3: Essential Components of Intelligent Agents Technical requirements Knowledge representation in intelligent agents Semantic networks Frames Logic-based representations Reasoning in intelligent agents Deductive reasoning Inductive reasoning Abductive reasoning Learning mechanisms for adaptive agents Decision-making and planning in agentic systems Utility function Planning algorithms Enhancing agent capabilities with generative AI Start building agentic AI Summary Questions Answers Part 2: Designing and Implementing Generative AI-Based Agents Chapter 4: Reflection and Introspection in Agents Technical requirements The importance of reflection in agents Enhanced decision-making Adaptation Ethical consideration Human-computer interaction Introspection in intelligent agents Implementing reflective capabilities Traditional reasoning Meta-reasoning Self-explanation Self-modeling Use cases and examples Customer service chatbots Personal marketing agents Financial trading systems Forecast agents Price strategies in e-commerce Summary Questions Answers Chapter 5: Enabling Tool Use and Planning in Agents Technical requirements Understanding the concept of tool use in agents Tool and function calling Defining tools for agents Types of tools The significance of tools in agentic systems Planning algorithms for agents Less practical planning algorithms Moderately practical planning algorithm – FF Most practical planning algorithms Integrating tool use and planning Reasoning about tools Planning for tool use Exploring practical implementations CrewAI example AutoGen example LangGraph example Summary Questions Answers Chapter 6: Exploring the Coordinator, Worker, and Delegator Approach Technical requirements Understanding the CWD model Key principles of the CWD model The CWD model for the intelligent travel agent Designing agents with role assignments Roles and responsibilities of each agent Communication and collaboration between agents Communication Coordination mechanism Negotiation and conflict resolution Knowledge sharing Implementing the CWD approach in generative AI systems System prompts and agent behavior Instruction formatting Interaction patterns Summary Questions Answers Chapter 7: Effective Agentic System Design Techniques Technical requirements Focused system prompts and instructions for agents Defining objectives Task specifications Contextual awareness State spaces and environment modeling State space representation Environment modeling Integration and interaction patterns Monitoring and adaptation Agent memory architecture and context management Short-term memory (working memory) Long-term memory (knowledge base) Episodic memory (interaction history) Context management Integration with decision-making Sequential and parallel processing in agentic workflows Sequential processing Parallel processing Workflow optimization Summary Questions Answers Part 3: Trust, Safety, Ethics, and Applications Chapter 8: Building Trust in Generative AI Systems Technical requirements Importance of trust in AI Techniques for establishing trust Transparency and explainability Dealing with uncertainty and biases Effective output communication User control and consent Ethical development and responsibility Implementing transparency and explainability Handling uncertainty and biases Summary Questions Answers Chapter 9: Managing Safety and Ethical Considerations Understanding potential risks and challenges Adversarial attacks Bias and discrimination Misinformation and hallucinations Data privacy violations Intellectual property risks Ensuring safe and responsible AI Exploring ethical guidelines and frameworks Human-centric design Accountability and responsibility Privacy and data protection Involvement of diverse stakeholders Addressing privacy and security concerns Summary Questions Answers Chapter 10: Common Use Cases and Applications Creative and artistic applications Evolution of creative and artistic agents Real-world applications Natural language processing and conversational agents Evolution of language agents Real-world applications Robotics and autonomous systems Evolution of robotic agents Real-world applications Decision support and optimization Evolution of decision support agents Real-world applications Summary Questions Answers Chapter 11: Conclusion and Future Outlook Recap of key concepts Emerging trends and research directions Multi-modal intelligence – integrating diverse inputs Advanced language comprehension Experiential learning – reinforcement learning innovations Practical implications across industries Artificial general intelligence What makes AGI different The big challenge Learning to learn Real-world understanding Challenges and opportunities Summary Index Other Books You May Enjoy