Building AI Agents: From Design Patterns to Production
暫譯: 打造 AI Agent：從設計模式到正式環境佈署
Gulli, Antonio, Nawalgaria, Anant
- 出版商: Springer
- 出版日期: 2026-11-07
- 售價: $1,960
- 貴賓價: 9.5 折 $1,862
- 語言: 英文
- 頁數: 324
- 裝訂: Quality Paper - also called trade paper
- ISBN: 3032390958
- ISBN-13: 9783032390950
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相關分類:
Large language model、LangChain、Design Pattern
海外代購書籍(需單獨結帳)
相關主題
商品描述
Most AI agent demos work flawlessly, until real users arrive. The gap between a compelling prototype and a dependable system is where this book begins. Framing agent development as an engineering discipline, it centers on the core loop of Perceive, Plan, Act, and Observe as the foundation for building agents that are robust, interpretable, and scalable.
As a companion to Antonio Gulli's Agentic Design Patterns, Building AI Agents carries the field's emerging architectural vocabulary from concept into practice. Where the earlier volume defines the patterns, this book implements them: showing how to translate agent design principles into reliable, production-ready systems.
Organized around four proven architectural patterns -- ReAct, Chain-of-Thought, Reflection, and Plan-and-Execute -- the book takes a hands-on, pattern-first approach. Each chapter includes working code and contributes to the development of Atlas, a unified research and coding assistant that evolves from a minimal script into a production-grade multi-agent system.
Coverage spans tool integration, memory and state management, multi-agent orchestration, and system evaluation. Implementations are demonstrated across leading frameworks, including LangGraph, CrewAI, OpenAI's Agents SDK, and Google's ADK, with designs that generalize across major model providers such as OpenAI, Gemini, Claude, and Llama. The emphasis throughout is on transferable patterns rather than vendor-specific solutions.
This book is intended for software engineers integrating agents into production systems, AI/ML practitioners moving beyond chat-based interfaces, technical leads evaluating architectural tradeoffs, and advanced students working at the frontier of applied AI. Readers should be comfortable with Python and have a foundational understanding of large language models; all other concepts are developed in context.
商品描述(中文翻譯)
大多數 AI agent 示範都能完美運作，直到真正的使用者開始使用。從引人注目的原型到值得信賴的系統之間，存在一段落差，而本書正是從這裡開始。全書將 agent 開發定位為一門工程學科，並以 Perceive（感知）、Plan（規劃）、Act（執行）與 Observe（觀察）這個核心迴圈為基礎，建構健壯、可解釋且可擴充的 agents。
作為 Antonio Gulli《Agentic Design Patterns》的配套讀物，《Building AI Agents》將這個領域逐漸形成的架構詞彙，從概念帶入實務。前一本著作定義了各種模式，本書則進一步實作這些模式，示範如何將 agent 設計原則轉化為可靠、可投入正式環境的系統。
本書以四種經驗證的架構模式為組織核心：ReAct、Chain-of-Thought、Reflection，以及 Plan-and-Execute，採用實作導向、以模式為先的學習方式。每章都包含可運作的程式碼，並共同推進 Atlas 的開發。Atlas 是一款整合式研究與程式設計助理，將從最精簡的腳本逐步演進為具備正式環境品質的 multi-agent system（多代理系統）。
涵蓋內容包括工具整合、記憶體與狀態管理、multi-agent orchestration（多代理協調），以及系統評估。書中將透過多個主流 framework 展示實作，包括 LangGraph、CrewAI、OpenAI's Agents SDK 與 Google's ADK；相關設計也能泛用於 OpenAI、Gemini、Claude 和 Llama 等主要模型供應商。全書始終強調可跨平台移植的模式，而非特定廠商專屬的解決方案。
本書適合將 agents 整合至正式環境系統中的 software engineers（軟體工程師）、希望超越聊天介面的 AI/ML practitioners（AI/ML 實務工作者）、評估架構取捨的 technical leads（技術主管），以及投入應用 AI 前沿研究的進階學生。讀者應熟悉 Python，並具備 large language models（大型語言模型）的基礎理解；其餘概念都會在相關脈絡中逐步介紹。
作者簡介
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.
Anant Nawalgaria is a Senior Staff Machine Learning Architect and Product Leader at Google. Specializing in bridging the gap between cutting-edge AI research and scalable production, his work spans generative AI, multi-agent systems, and novel architectures for multimodal LLMs. Anant has co-authored research spotlighted at prestigious conferences such as ICLR and ICML.
At Google, Anant drives 0-to-1 research and product creation, leading pioneering technologies like Gecko, a state-of-the-art multimodal evaluation framework, and AlphaEvolve, an advanced evolutionary coding agent utilizing black-box optimization. He has also contributed to core enhancements of early Gemini models, researching novel architectures for performance and cost optimization in multimodal LLMs. Beyond research, Anant leverages his forward-deployment engineering expertise to guide some of the world's largest, most innovative enterprises in building and scaling cutting-edge AI solutions for novel production use cases.
Anant is also a prominent technical educator and industry voice. He founded and led the global Google x Kaggle Generative AI course series, reaching over two million learners worldwide, has spoken at numerous industry-wide events, and publishes highly popular technical blogs and white papers on generative AI, agents, and vibe coding. Additionally, he serves as a contributor and reviewer for multiple authoritative books on applied machine learning and agentic systems. Anant holds a Master of Science in Informatics (with Distinction) from the Technical University of Munich (TUM) and an MBA from the Indian Institute of Technology Madras (IIT-M).
作者簡介(中文翻譯)
Antonio Gulli 是 Google 資深總監，目前在 CTO 辦公室擔任工程總監。他擁有超過 30 年的相關經驗，是業界知名人士，在 AI、Search 與 Cloud 技術方面具備深厚背景。Antonio 擁有豐富的技術團隊管理經驗，並曾在 EMEA 地區提供 Google Cloud 技術解決方案。他過去曾擔任 Google 的站點主管（Site Lead）與工程總監（Engineering Director），負責管理 Cloud 團隊，並與國際據點及銷售部門密切合作，帶領跨職能團隊。Antonio 也曾著有《Deep Learning for Keras》一書，致力於提升科學文化意識。
Antonio 的學歷背景十分傑出，擁有比薩大學（University of Pisa）電腦科學博士學位，以及同校工程碩士學位。他也持有比薩大學的實務工程碩士學位，以及該校的電腦科學學士學位。Antonio 的技術專長包括資深軟體工程、AI、Search、Cloud、Kubernetes、Keras 與 Deep Learning。他同時擔任董事會成員及創投顧問（VC Advisor），是任何組織都極具價值的人才。
Anant Nawalgaria 是 Google 的資深首席機器學習架構師（Senior Staff Machine Learning Architect）與產品領導者。他專注於銜接尖端 AI 研究與可擴展的正式環境應用，研究領域涵蓋生成式 AI、多代理系統，以及多模態 LLM 的新型架構。Anant 曾共同撰寫研究成果，並在 ICLR 與 ICML 等知名會議中受到重點介紹。
在 Google，Anant 負責推動從 0 到 1 的研究與產品創新，領導 Gecko 等先進技術的開發。Gecko 是最先進的多模態評估框架之一；此外，他也帶領 AlphaEvolve 的開發，這是一種採用黑箱最佳化的先進演化式程式設計代理。他也參與早期 Gemini 模型的核心強化工作，研究多模態 LLM 的新型架構，以提升效能並最佳化成本。除了研究工作之外，Anant 也運用其前進部署工程（forward-deployment engineering）專長，協助全球規模最大且最具創新性的企業，為全新的正式環境應用案例打造並擴展尖端 AI 解決方案。
Anant 同時也是傑出的技術教育者與業界意見領袖。他創辦並領導全球性的 Google x Kaggle Generative AI 課程系列，已觸及全球超過兩百萬名學習者；他曾在許多業界大型活動中發表演講，也持續發表廣受歡迎的生成式 AI、代理（agents）與 vibe coding 技術部落格及白皮書。此外，他也擔任多本應用機器學習與代理系統（agentic systems）權威書籍的貢獻者與審稿人。Anant 擁有慕尼黑工業大學（Technical University of Munich，TUM）資訊學理學碩士學位（優等），以及印度理工學院馬德拉斯分校（Indian Institute of Technology Madras，IIT-M）的 MBA 學位。
