Agentic Design Patterns

This repository contains the full text of the book "Agentic Design Patterns" by Antonio Gulli and Mauro Sauco. The content has been compiled and organized by Tom Mathews for easy access and reference for the community.

Authorship and Credit

• Authors: Antonio Gulli and Mauro Sauco
• Compiled by: Tom Mathews

What makes this book stand out?

This 424-page guide tackles the real challenges we face when building intelligent, autonomous AI systems. It bridges the gap between theory and implementation—exactly what our field needs right now. This is the best resource for anyone serious about building real AI systems. If you're an engineer, researcher, or product manager ready to move beyond basic LLM applications and build truly robust AI agents, this is for you.

The book covers essential agentic patterns including Prompt Chaining, Routing, Planning, and Multi-Agent Systems, all with practical, code-based examples. You'll find comprehensive coverage of Tool Use, Memory Management, and RAG implementation, plus advanced topics like Reasoning Techniques and Inter-Agent Communication.

Inside you will find:

• Real code examples: Not just theory, but working implementations.
• Proven patterns: Memory handling, exception logic, resource control, safety guardrails.
• Advanced techniques: Multi-agent orchestration, inter-agent messaging, human-in-the-loop.
• Full chapter on MCP (Model Context Protocol): A key framework for integrating tools with agents.

It covers 21 core patterns across 4 sections:

1. Foundational patterns (prompt chaining, routing, tool use)
2. Advanced systems (memory, learning, monitoring)
3. Production concerns (error handling, safety, evaluation)
4. Multi-agent architectures

Most AI content stops at “how to call an API.” But in real-world systems you need to ask:

• What if the agent gets stuck mid-task?
• How do you preserve memory across long sessions?
• How do you prevent chaos when you run 10+ agents?

This book answers all that with patterns you can actually apply. The 70+ page appendix alone is worth the investment, featuring Advanced Prompting techniques and an overview of Agentic Frameworks.

Table of Contents

Introduction

• Dedication
• Acknowledgment
• Foreword
• A Thought Leader's Perspective: Power and Responsibility
• Introduction
• What makes an AI system an Agent?

Part One: Foundational Patterns

• Chapter 1: Prompt Chaining
• Chapter 2: Routing
• Chapter 3: Parallelization
• Chapter 4: Reflection
• Chapter 5: Tool Use (Function Calling)
• Chapter 6: Planning
• Chapter 7: Multi-Agent Collaboration

Part Two: Advanced Systems

• Chapter 8: Memory Management
• Chapter 9: Learning and Adaptation
• Chapter 10: Model Context Protocol (MCP)
• Chapter 11: Goal Setting and Monitoring

Part Three: Production Concerns

• Chapter 12: Exception Handling and Recovery
• Chapter 13: Human in the Loop
• Chapter 14: Knowledge Retrieval (RAG)

Part Four: Multi-Agent Architectures

• Chapter 15: Inter-Agent Communication (A2A)
• Chapter 16: Resource-Aware Optimization
• Chapter 17: Reasoning Techniques
• Chapter 18: Guardrails and Safety Patterns
• Chapter 19: Evaluation and Monitoring
• Chapter 20: Prioritization
• Chapter 21: Exploration and Discovery

Appendix

• Appendix A: Advanced Prompting Techniques
• Appendix B: AI Agentic Interactions: From GUI to Real-World Environment
• Appendix C: Quick Overview of Agentic Frameworks
• Appendix D: Building an Agent with AgentSpace (online only)
• Appendix E - AI Agents on the CLI
• Appendix F: Under the Hood: An Inside Look at the Agent's Reasoning Engines
• Appendix G: Coding Agents

License

This repository is licensed under the MIT License.