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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.