Most of the content about building AI agents today feels incomplete. It either focuses on simple API tutorials that don't scale or on complex theories that don't translate to production environments. Building the foundation of AI agents at AgentDock and implementing these systems across real-world environments, we've seen firsthand where impressive demos fall apart under production pressure.
To help bridge this gap, we've decided to open-source our playbook. This post marks the beginning of the AI Agents Book, a comprehensive guide that will grow into a full-fledged resource for building reliable, production-ready AI agents.
Read Chapter 1: Why AI Agents Matter →
This book is a reflection of our direct experience building and deploying agentic systems. We're not interested in repeating basic concepts you can find elsewhere. Instead, our focus is on the practical challenges and architectural patterns that matter when you move from a prototype to a live system.
We believe the AI agent space needs more shared knowledge grounded in actual implementation. Too many developers are forced to rediscover the same failure modes and operational challenges. By sharing what we've learned-often the hard way-we hope to help others build more robust and effective systems.
The book is designed to be a living document that evolves with the technology and our understanding. It begins with the foundational principles of production-ready agents and will expand to cover advanced topics, enterprise-scale deployment, and specific industry applications.
Our plan is to finalize the book by releasing a chapter every Wednesday, making sure it stays evergreen in the ever-developing AI ecosystem. Once complete, we'll publish it and make it available as a hard copy (hopefully to send to our early backers!).
You can explore the current structure and planned chapters on the main book page.
While the book covers universal methods and patterns applicable across the ecosystem, we use our open-source framework, AgentDock Core, for many of the code examples. We do this simply because we know it inside and out, which allows us to provide clear and practical illustrations of the concepts discussed. Our goal is to teach the underlying principles, not to lock you into a specific tool.
We're starting with what we know is essential for building agents that don't just work in demos, but deliver real value in production. We invite you to follow along as we build out this resource.