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.

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).