About the Book
The Agentic AI Book is the definitive guide for anyone serious about understanding the core components of AI agents and building ones that actually work in the real world.
While others show you how to build agents in low-code environments and hope for the best, this book reveals why agents fail and how to fix them. You'll understand the cognitive architecture behind agent behavior, and learn to diagnose hallucinations, prevent prompt injections, and handle the chaos of non-deterministic systems.
What Sets This Book Apart
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Foundation First, Problems Second, Solutions Third.
Traces AI from n-grams to transformers, from rule-based systems to emergent intelligence. Each chapter builds from first principles—you'll understand not just what works, but why. Perfect for AI newcomers and experts alike. -
Battle-Tested Production Wisdom.
Learn from real failures: why multi-agent systems deadlock, when RAG backfires, why demo agents crash in production. Includes concrete strategies for debugging non-deterministic behavior, monitoring, security, and scaling. -
Beyond the Hype Cycle.
No AGI promises—just honest assessments of current capabilities and practical patterns that work today. From ReAct loops to guardian agents, from LangGraph orchestration to enterprise integration.
Who This Book Is For
Whether you're a technical leader evaluating AI strategies, an engineering manager building agent teams, a software developer transitioning to AI, a data scientist expanding beyond models, or a machine learning engineer ready to deploy autonomous systems—this book gives you both the foundational understanding and practical tools to succeed.
Perfect for those new to AI who want deep understanding, and invaluable for experienced practitioners who need production-grade solutions.
What AI Leaders Say
"Dr. Rad’s deep dive into the architectural evolution from manual feature engineering to the representational revolution of deep learning is essential reading. He moves past surface-level tutorials to explain the fundamental shift from hard logic to 'learned intuition'. This book provides the technical substrate needed to move beyond simple 'prompt-and-pray' methods to the D2D reality of production-ready agentic systems."
"Finally, a resource that respects the reader's intelligence. Ryan cuts through the noise of the current AI hype cycle with surgical precision. By establishing the Three-Layer Framework—Objectives, Training, and Architecture—he provides a coherent mental model that finally eliminates the confusion caused by overlapping AI terminology. His explanation of the 'VLM Trick'—efficiently connecting pre-trained vision encoders to LLMs—is the most practical guide available for building agents that can truly perceive and interact with the visual world."
Buy the Book
Chapters
Full Completion: July 2026
I am releasing this book incrementally to ensure it reflects the latest breakthroughs. Chapters 1 and 2 (35%-40% of the book) are available now exclusively for Founding Members.
- Chapter 1: The AI Landscape (Done ✔)
- Chapter 2: Language Models and Multimodal Intelligence (Done ✔)
- Chapter 3: Building with Large Language Models (Late March)
- Chapter 4: Agent Building Blocks (Late April)
- Chapter 5: Multi-Agent Architectures and Design Patterns (Late May)
- Chapter 6: Production-Ready Agentic AI (Late June)
This book follows a Community-First publishing model. Founding Members receive monthly updates and the final edition in July at no extra cost.
About the Author
Dr. Ryan Rad is an AI researcher, professor, and industry advisor with 15 years shaping artificial intelligence. He has architected transformative academic programs for leading universities—from Generative AI curricula to MBA Analytics pathways—while scaling AI solutions across 40 countries for Fortune 500 companies.
Standing at the intersection of academia, research, and industry, Dr. Rad is a sought-after international speaker and trusted voice for both universities building AI programs and enterprises navigating adoption. His forthcoming book distills 15 years of building, teaching, and deploying AI systems into actionable insights for tomorrow's AI leaders.
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