AI Agents Book
The definitive guide to building production-ready AI agents. Comprehensive coverage from fundamentals through enterprise deployment, focusing on practical patterns and real-world implementation.
TL;DR: A comprehensive guide to building AI agents that actually work in production. This evergreen resource covers everything from fundamentals through enterprise deployment, focusing on practical patterns and real-world implementation.
Building the foundational understanding of AI agents, from core concepts to production realities.
| Chapter | Topic | Status |
|---|---|---|
| Chapter 1 | Why AI Agents Matter | ✅ Available |
| Chapter 2 | The Technical Reality of Production AI Agents | ✅ Available |
| Chapter 3 | Where AI Agents Actually Work in Production | Coming Soon |
| Chapter 4 | The Production Reality Gap: What They Don't Tell You | Coming Soon |
| Chapter 5 | The Future of AI Agents: From Hype to Production | Coming Soon |
Deep dives into sophisticated patterns for real-world agent systems.
| Chapter | Topic | Status |
|---|---|---|
| Chapter 6 | Beyond Basic RAG | Coming Soon |
| Chapter 7 | Complex Workflow Design | Coming Soon |
| Chapter 8 | Multi-Agent Coordination | Coming Soon |
| Chapter 9 | Performance Reasoning | Coming Soon |
Scaling agents to handle business-critical workloads and enterprise requirements.
| Chapter | Topic | Status |
|---|---|---|
| Chapter 10 | Memory and Learning Systems | Coming Soon |
| Chapter 11 | Business-Critical Deployment | Coming Soon |
| Chapter 12 | Scale and Enterprise Integration | Coming Soon |
| Chapter 13 | MCP and Future Developments | Coming Soon |
Real-world implementations across specific industries and use cases.
- Healthcare, Finance, Legal, Manufacturing
AI enables anyone to build AI agents, but production-ready systems require engineering discipline.
This book is for:
- Developers who need agents that work reliably at scale
- Technical Leaders evaluating AI agent adoption
- Engineers moving beyond basic chatbot integrations
- Anyone serious about production AI systems
Core Concepts: The four pillars of agency (Perception, Reasoning, Planning, Action), architectural patterns that separate creative from deterministic functions, framework tradeoffs and implementation choices, and production reliability engineering.
Practical Implementation: Error handling, circuit breakers, graceful degradation, token optimization, cost management, security, authentication, compliance, monitoring, and observability patterns.
Real Examples: Working code demonstrating medical diagnostic agents with systematic reasoning, legal analysis systems with compliance checking, financial agents with audit trails, and enterprise automation workflows.
- Read: Start with Chapter 1: Why AI Agents Matter
- Explore: Browse 40+ agent templates and live examples
- Build: Fork the repository to build your own agents
- Basic understanding of APIs and webhooks
- Any programming language experience helpful
- Familiarity with at least one LLM API
Each chapter includes comprehensive written content with clear explanations, production-ready code examples, and video walkthroughs. For more advanced features, including a visual agent creator and unified workflow automation, see AgentDock Pro.
After talking with hundreds of developers struggling with AI agents, we identified the gap between impressive demos and production reality. This book bridges that gap with:
- Framework-agnostic patterns that work anywhere
- Battle-tested solutions from real deployments
- Honest tradeoff discussions for technical decisions
- Focus on reliability over complexity
We welcome contributions:
- Production agent implementations
- Framework analysis studies
- Deployment patterns
- Performance optimizations
After talking with hundreds of people consuming existing AI agent courses and resources, we identified what developers actually need to bridge the gap between temp and production systems. This book is based on analysis of real production deployments and lessons learned from:
- 100+ production agent implementations across industries
- Multiple framework comparisons in identical use cases
- Enterprise compliance and security audits
- Cost optimization case studies
- AgentDock Core Docs - TypeScript implementation guide
- Agent Templates - 40+ working examples
- Getting Started Guide - Quick setup instructions
For enterprise and advanced use cases:
- Why AgentDock - Framework advantages and design philosophy
- AI Agents Overview - Comprehensive introduction to agent concepts
- Request for Agents - Community-driven development priorities
- ⭐ Star this repository to track updates
- 🔔 Watch releases for new chapter notifications
- 🚀 Try the examples in our agent templates
- 💬 Join discussions to ask questions and share insights
As we release new chapters, each will include:
- Comprehensive written content with clear explanations
- Video walkthroughs of key concepts
- Production-ready code examples
- Community highlights and real-world implementations
Ready to build agents that actually work? Begin with Chapter 1: Why AI Agents Matter →