About the Book
AI content is everywhere — but most of it is engineered for infotainment, not careers. The barrier to entry has shattered, yet the gap in understanding has never been wider. Anyone can spin up an agent in an afternoon, but when it breaks in production, most people have no idea why.
The Agentic AI Book is the answer to that gap. Written by an active AI professor and industry advisor, it bridges the distance between academic theory and production-ready reality. This isn't a collection of tutorials — it's a framework for engineers who want to leave the 'prompt-and-pray' era behind. You'll understand the cognitive architecture driving agent behavior, and learn to diagnose hallucinations, prevent prompt injections, and architect systems that survive contact with non-deterministic environments.
What Sets This Book Apart
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Visual Blueprints for Complex Logic.
Features over 120 original, custom-built figures. I've mapped out the complex architectures visually so you don't have to reverse-engineer dense academic papers to understand what's actually happening under the hood. -
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. -
Beyond the Hype Cycle.
A new buzzword, framework, or trick pops up every week — and most of them will be forgotten in months. This book ignores the noise and focuses ruthlessly on what stays: attention mechanisms, multi-agent coordination, RAG, and the core patterns that have been battle-tested across real systems. We're studying the physics, not the weather.
📖 Inside the Book
Not surface-level tutorials. Not "vibes-based" prompting. A Foundations-First curriculum built for the D2D (Design to Deployment) reality of agentic systems.
- Foundations: Master the Three-Layer Framework (Architecture, Training, Objective) to deconstruct any model—from n-grams to GPT-4o. Understand the death of Hard Logic, the rise of Learned Intuition, and the Scaling Laws that define the physics of modern AI.
- LLMs & VLMs: Diagnose the Reversal Curse, the Modality Gap, and Perception-Reasoning Dissociation in vision models—learning when a model sees but doesn't understand. Learn why Inference-Optimal Overtraining means a smaller model can outperform a giant.
- Adaptation & Reliability: When prompts fail, renovate the weights. Master chunking strategies, the RAG Triad, LoRA/QLoRA, and the alignment frontier—DPO and GRPO, the secret sauce behind today's top reasoning models.
- Cognitive Architecture: Define autonomy through Control Flow with the 6 Levels of Agentic Autonomy. Apply Poka-Yoke principles to tool design via the ACI, and fight Context Rot with JIT Context and Attention Budget management.
- Multi-Agent Systems: Orchestrate agent societies using DAG-based task graphs and the AAI. Know when not to scale (the Complexity Tax), slash costs with Semantic Compression, and prevent deadlocks with Conflict Resolution Protocols.
- Production Reality (D2D): Replace hope with Reliability Engineering. Manage the Reasoning Budget, master Quantization and Speculative Sampling for efficient deployment, implement guardrails and observability, and debug Agentic Race Conditions before they reach your users.
Who This Book Is For
Most AI books are either too academic — written for researchers — or too shallow — written for people who want to feel smart at dinner parties. There's very little in between for the serious practitioner who actually needs to build and deploy. This book fills that gap.
- Software developers transitioning to AI — build a real mental model, not just API wrappers.
- ML engineers ready to deploy autonomous systems — go from notebook to production.
- Technical leaders evaluating AI strategies — separate bedrock concepts from hype.
- Data scientists expanding beyond models — understand the full agent architecture.
Perfect for those new to AI who want deep understanding, and invaluable for experienced practitioners who need production-grade solutions.
Frequently Asked Questions
What is "Founding Member" access?
It is a community-first model that allows you to lock in Value-Based Pricing before the final launch. The book is nearing completion this May, and this founding offer is only available for a limited time. By joining now at 77% completion, you can secure lifetime access for as low as $18 before the price increases to the ~$50 retail mark.
Does early access include the final edition?
Yes. Founding Members receive Lifetime Access, which includes all monthly chapter updates, the final digital edition in late May, and exclusive bonuses like access to the private code repository and real-world case studies.
Will I get a discount on the physical book?
YES! All Founding Members will receive a special discount code for the physical print version (Hardcover/Paperback) when it officially launches on Amazon, currently estimated for June 2026.
What is the book's roadmap to completion?
The book is currently 77% complete and we have been always 1-2 weeks ahead of the schedule. Chapters 1–4 are available now. Chapter 5 (Multi-Agent Architectures) is scheduled for late-April, and Chapter 6 (Production Reality) for late-May. The full completion and Amazon launch are set for June 2026.
How deep does the technical content go?
The book moves beyond "demo-grade" agents to production-ready mastery. It covers the full stack: from foundational Transformers and attention mechanisms to advanced RAG strategies, VLM "tricks," and multi-agent orchestration patterns designed to prevent deadlocks and runaway costs, and much more.
Can I check it out before I buy?
Absolutely. You can download The Agentic AI Sampler — a massive 70+ page preview. Unlike a standard preview that only shows the intro, this curated selection includes the most of the foundations from Chapters 1 & 2 plus exclusive deep-dive content from later chapters, giving you a holistic view of the book's technical depth.
Buy the Book
Chapters
Full Completion: June 2026
The demand for a comprehensive, practical guide to agentic AI is now. I'm releasing this book incrementally so you can start reading immediately. Chapters 1-4 (70% 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 (Done ✔)
- Chapter 4: Agent Building Blocks (Done ✔)
- Chapter 5: Multi-Agent Architectures and Design Patterns (Late Apr)
- Chapter 6: Production-Ready Agentic AI (Late May)
This book follows a Community-First publishing model. Founding Members receive new chapters as they're written, plus the final edition in July at no extra cost.
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. 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."
About the Author
Dr. Ryan Rad is an AI researcher, professor, and industry advisor with over 15 years shaping how artificial intelligence is built, taught, and deployed. He holds faculty positions at Northeastern University and the University of British Columbia, where he has designed curricula and graduate programs in Machine Learning, Computer Vision, Generative AI, and Data Science—reaching thousands of students across six universities and online. His industry work spans research, engineering, and leadership at startups and tech giants including Microsoft, scaling AI solutions across 40 countries for Fortune 500 companies.
A sought-after speaker with 50+ international talks and tens of top-tier peer-reviewed publications, Dr. Rad's research focuses on resource-efficient generative AI for edge deployment. He is the author of The Agentic AI Book—distilling years of building, teaching, and deploying AI into actionable insights—and shares AI perspectives on his YouTube channel.
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