Leanpub Book LAUNCH 🚀 The Agentic AI book: From Language Models to Multi-Agent Systems by Dr. Ryan Rad
The Agentic AI Book is the definitive engineering guide for practitioners who want to move past fragile "prompt-and-pray" scripts, understand exactly why agents work and fail, and architect autonomous systems that hold up under real-world production conditions.
Welcome to the Leanpub Launch video for The Agentic AI book: From Language Models to Multi-Agent Systems by Dr. Ryan Rad!
Dr. Ryan Rad
About the Book
⭐ Bestseller in Deep Learning and Generative AI ⭐
The barrier to building autonomous AI systems has completely collapsed, but the chasm in true engineering understanding has never been deeper.
The demo survives the conference room. The system does not survive the week.
The Agentic AI Book is the definitive engineering guide for practitioners who want to move past fragile "prompt-and-pray" scripts, understand exactly why agents work and fail, and architect autonomous systems that hold up under real-world production conditions. This book takes you from language model foundations to production-ready multi-agent systems, with enough depth to predict failure modes before they surface, design systems that degrade gracefully rather than catastrophically, and diagnose exactly what broke and why when they do.
📖 Inside the Book
Each section builds from first principles to complex orchestration, covering not just what these systems do, but how they work architecturally.
- Build intuition that decodes any model — past, present, or future. Trace the complete problem-solving arc from bag-of-words to self-attention, understanding why each breakthrough was necessary and what failure it fixed. Master the Three-Layer Framework as a universal evaluation lens, and understand scaling laws deeply enough to explain why a properly overtrained smaller model can outperform a frontier giant.
- Diagnose the failures that surface-level AI education never names. Move past "the model hallucinated" to isolate specific, actionable failure modes: the Reversal Curse, Flat Latency, and Underspecification in language systems; the Modality Gap and Perception-Reasoning Dissociation in multimodal ones. Coverage extends to the full VLM stack.
- Adapt models when prompting reaches its limits. Master RAG architectures, the RAG Triad, LoRA and QLoRA, and the alignment frontier — including DPO and GRPO, powering today's leading reasoning models.
- Defeat context rot and design bulletproof tools. Navigate the Six Levels of Agentic Autonomy, apply Poka-Yoke principles to the Agent-Computer Interface, and build three-tier memory systems with Just-In-Time context loading and attention budget management.
- Orchestrate multi-agent systems without paying the complexity tax. Use DAG-based task graphs and the Agent-to-Agent Interface, know when not to scale, and prevent deadlocks and runaway costs with conflict-resolution protocols and semantic compression.
- Take absolute engineering ownership of production deployment. Replace hope with reliability engineering. Implement evaluation frameworks, build observability stacks, enforce guardrail architectures, debug agentic race conditions, and deploy with quantization and speculative sampling. Design kill switch protocols before you need them.
👤 Who This Book Is For
This book is for serious practitioners who aren't strangers to code or machine learning fundamentals. If you occupy an ML-adjacent role — software engineer, data scientist, or technical leader — or you already have a foundational understanding of machine learning and want to bridge the gap to mastering LLMs and autonomous agents, this book was written for you. Not to impress you. To equip you.
💡 Why This Book
Most material on Generative and Agentic AI falls into one of three traps: surface-level infotainment engineered for quick consumption, technically fragmented and disconnected from first principles, or written for readers who already hold a PhD. This book is none of those things. It is a rigorous, framework-agnostic Design-to-Deployment lifecycle built by an author who has spent two decades watching production systems fail in ways that demos never predict.
Context engineering, memory tiering, reasoning-action loops, dynamic task decomposition, multi-agent orchestration: these are not hype. They are the physics of this field. Everything else is the weather of the week.
📚 Complete & Available Now
The book is complete and available now in PDF and EPUB formats, with print editions on Amazon.
Chapter 1: The AI Landscape ✓
Chapter 2: Language Models and Multimodal Intelligence ✓
Chapter 3: Building with Large Language Models ✓
Chapter 4: Agent Building Blocks ✓
Chapter 5: Multi-Agent Architectures & Design Patterns ✓
Chapter 6: Production-Ready Agentic AI ✓
💎 Get the Book
Digital Access: PDF and EPUB editions available now (you set the price), with lifetime access to all updates and bonus content.
Exclusive Bonus Content: Includes access to the companion code repository and real-world case studies.
Print Edition: Hardcover and paperback are available on Amazon from June 15. All digital members receive a discount code for the print edition.
Ready to move beyond demo-grade agents?
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.
Watch These Short Excerpts from the Full Video
