AI Agents in Practice: Design, implement, and scale autonomous AI systems for production

Master the art of building AI agents with this hands-on guide to orchestration, multi-agent systems, real-world case studies, and ethical insights to drive immediate business impact Key Features Build production-ready AI agents with hands-on tutorials for diverse industry applications Explore multi-agent system architectures with practical frameworks for orchestrator comparison Future-proof your AI development with ethical implementation strategies and security patterns Purchase of the print or Kindle book includes a free PDF eBook Book Description As AI agents evolve to take on complex tasks and operate autonomously, you need to learn how to build these next-generation systems. Author Valentina Alto brings practical, industry-grounded expertise in AI Agents in Practice to help you go beyond simple chatbots and create AI agents that plan, reason, collaborate, and solve real-world problems using large language models (LLMs) and the latest open-source frameworks. In this book, you'll get a comparative tour of leading AI agent frameworks such as LangChain and LangGraph, covering each tool's strengths, ideal use cases, and how to apply them in real-world projects. Through step-by-step examples, you’ll learn how to construct single-agent and multi-agent architectures using proven design patterns to orchestrate AI agents working together. Case studies across industries will show you how AI agents drive value in real-world scenarios, while guidance on responsible AI will help you implement ethical guardrails from day one. The chapters also set the stage with a brief history of AI agents, from early rule-based systems to today's LLM-driven autonomous agents, so you understand how we got here and where the field is headed. By the end of this book, you'll have the practical skills, design insights, and ethical foresight to build and deploy AI agents that truly make an impact. What you will learn Build core agent components such as LLMs, memory systems, tool integration, and context management Develop production-ready AI agents using frameworks such as LangChain with code Create effective multi-agent systems using orchestration patterns for problem-solving Implement industry-specific agents for e-commerce, customer support, and more Design robust memory architectures for agents with short- and long-term recall Apply responsible AI practices with monitoring, guardrails, and human oversight Optimize AI agent performance and cost for production environments Who this book is for This book is ideal for AI engineers and data scientists looking to move beyond basic LLM implementations to build sophisticated autonomous agents. Software developers and system architects will find practical guidelines for integrating agents into existing tech stacks. Product managers and technical entrepreneurs will gain strategic insights into how AI agents can solve business problems across industries. A basic understanding of machine learning concepts and working knowledge of Python are required to make the most of this book and implement production-ready AI agent systems. Table of Contents Evolution of GenAI Workflows The Rise of AI Agents The Need for an AI Orchestrator The Need for Memory and Context Management The Need for Tools and External Integrations Building Your First AI Agent with LangChain Multi-Agent Applications Orchestrating Intelligence: Blueprint for Next-Gen Agent Protocols Navigating Ethical Challenges in Real-World AI View Less