This book is a practical resource designed to help developers master the art of building sophisticated AI agents. As artificial intelligence evolves from simple reactive programs to autonomous entities capable of understanding context and making complex decisions, this book provides the essential Design Patterns and proven techniques needed to construct intelligent systems effectively. Each of the 21 Design Patterns represents a fundamental building block for creating agents that can perceive their environment, make informed decisions, and execute actions autonomously.

Agentic Design Patterns: A Hands-On Guide to Building Intelligent Systems is structured as a comprehensive hands-on guide, with each chapter dedicated to a single agentic pattern. Within each chapter, you will find a detailed pattern overview, practical applications and use cases, one or more hands-on code example, and key takeaways for quick review. From foundational concepts such as Prompt Chaining and Tool Use to advanced topics like Multi-Agent Collaboration and Self-Correction, readers will gain practical knowledge they can immediately apply. While the chapters build on each other, you can also use the book as a handy reference, jumping to patterns that address your specific challenges.

To provide a tangible "canvas" for the code examples, this guide utilizes three prominent agent development frameworks: LangChain and its extension LangGraph, which offer a flexible way to build complex operational sequences; Crew AI, which provides a structured framework for orchestrating multiple agents; and the Google Agent Developer Kit (Google ADK), which offers tools for building, evaluating, and deploying agents. By showcasing examples across these tools, you will gain a broad understanding of how these patterns can be applied in any technical environment.

Building effective agentic systems requires more than just a powerful language model; it demands structure and design. Agentic patterns provide reusable, battle-tested solutions to common challenges, much like design patterns in software engineering. They offer a common language that makes an agent's logic clearer, more maintainable, and more robust. By the end of this journey, you will possess both the theoretical understanding and the practical skills to implement these 21 essential patterns, enabling you to build more intelligent, capable, and autonomous systems on your chosen development canvas.

21 proven agentic patterns with runnable code for immediate, real-world application Gives practical guidance to build robust, autonomous AI agents from foundational to advanced levels Features framework-agnostic examples using LangChain, Crew AI, and Google ADK

Autorentext

Antonio Gulli is a highly experienced Senior Director at Google, currently leading the Engineer Director role in the Office of CTO. With over 30 years of relevant experience, Antonio is a well-known figure in the industry, with a strong background in AI, Search, and Cloud technologies. Antonio has extensive experience managing technical teams and providing Google Cloud technology solutions across EMEA industry. He has previously served as Site Lead and Engineering Director for Google, where he managed cloud teams and led cross-functional teams in strong collaboration with international sites and sales functions. Antonio has also authored the book "Deep Learning for Keras" to increase science culture awareness. Antonio's educational background is impressive, with a Ph.D. in Computer Science from the University of Pisa and a Master's degree in Engineering from the same university. He also holds a Master's degree in Practice Engineering from the University of Pisa and a Bachelor's degree in Computer Science from the University of Pisa. Antonio's technical expertise includes Senior Software Engineer, AI, Search, Cloud Kubernetes, Keras, and Deep Learning. He is also a Board Member and VC Advisor, making him a valuable asset to any organization.

Inhalt

Introduction.- Prompt Chaining.- Routing.- Parallelization.- Reflection.- Tool Use.- Planning.- Multi-Agent.- Memory Management.- Learning and Adaptation.- Model Context Protocol (MCP).- Goal Setting And Monitoring.- Exception Handling and Recovery.- Human-in-the-Loop.- Knowledge Retrieval (RAG).- Inter-Agent Communication (A2A).- Resource-Aware Optimization.- Reasoning Techniques.- Guardrails/Safety Patterns.- Evaluation and Monitoring.- Prioritzation.- Exploration and Discovery.- Appendix.