Mathews-Tom/Agentic-Design-Patterns is ranked 9,376 among these 8 peers; GauravBh1010tt/DeepLearn is ranked 9,377.

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. 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)-1bE4iMljhppqGY1p48gQWtZvk6MfRuJRCiba1yRykGNE. md) 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)-1e6XimYczKmhX9zpqEyxLFWPQgGuG0brp7Hic2sFl_qw. md) 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)-1v96Oobio6xDOqbK8ejsXjmOc4Dp2uoLMo5_gfJgi-NE. md) Part Four: Multi-Agent Architectures Chapter 15: Inter-Agent Communication (A2A)-1H6HmUYcy5kugt5gt7Kh2Zzb8C62d5pu36RsgMNDCX24. md) 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)-1bDRJ8mKtLTeWNC-cGD0Cr8pEJQgJHNcjqz5ekloAjaE. md) Appendix E