TLDR: Antonio Gulli, a distinguished engineer at Google, has authored a free 400-page technical guide titled “Agentic Design Patterns: A Hands-On Guide to Building Intelligent Systems.” Scheduled for release on December 3, 2025, the guide provides 21 design patterns for building autonomous AI systems, covering foundational to advanced concepts and utilizing frameworks like LangChain, CrewAI, and Google ADK. The publication aims to bridge a critical gap in AI development, offering structured blueprints for robust, real-world AI applications.
Google Distinguished Engineer Antonio Gulli has announced the upcoming release of a comprehensive 400-page technical guide, “Agentic Design Patterns: A Hands-On Guide to Building Intelligent Systems.” The free guide, set to be published on December 3, 2025, aims to provide developers with essential blueprints for creating sophisticated and autonomous artificial intelligence systems. This significant publication addresses what Gulli identifies as a critical gap in current AI development methodologies.
Gulli, a Senior Director and Distinguished Engineer in Google’s CTO Office, brings over 30 years of experience in AI, Search, and Cloud technologies to this work. He holds a Ph.D. in Computer Science from the University of Pisa and is also the author of “Deep Learning for Keras.” His extensive background underscores the depth and practical relevance of the new guide.
The guide introduces 21 distinct ‘agentic patterns,’ which serve as fundamental building blocks for intelligent, goal-oriented AI systems. These patterns range from foundational concepts such as ‘Prompt Chaining’ and ‘Tool Use’ to more advanced implementations like ‘Multi-Agent Collaboration’ and ‘Self-Correction’ frameworks. Each pattern offers a reusable, battle-tested solution to common challenges encountered in building complex AI agents.
According to Gulli, “It’s about moving from raw capability to robust, real-world applications.” He emphasizes that building effective agentic systems requires more than just a powerful language model; it demands structured architectural design. The book’s practical methodology dedicates each chapter to a single pattern, providing overviews, use cases, and hands-on code examples.
To ensure broad applicability, the guide details technical specifications across multiple prominent implementation frameworks. These include LangChain and its extension LangGraph for constructing complex operational sequences, CrewAI for orchestrating multiple agents, and the Google Agent Developer Kit (Google ADK) for evaluation and deployment processes. This multi-framework approach ensures that developers can apply these patterns across diverse technical environments.
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The economic implications of agentic AI are substantial. Recent research cited by PPC Land indicates that Google Cloud projects the agentic AI market could reach an impressive $1 trillion by 2040, with an anticipated 90% enterprise adoption. This highlights the growing importance and potential impact of the structured development approaches outlined in Gulli’s guide.
