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Full Description
Master AI agent development with this hands-on guide covering orchestration tools, multi-agent systems, real-world examples, ethical considerations, and practical implementations for immediate business impact.
Key Features
Build production-ready AI agents with hands-on tutorials for diverse industry applications
Master multi-agent system architectures with practical orchestrator comparison frameworks
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 DescriptionIn an era where AI agents are expected to operate autonomously and tackle complex tasks, AI Agents in Practice is your roadmap to building these next-generation systems. This book helps you go beyond simple chatbots and create AI agents that plan, reason, collaborate, and solve real-world problems using large language models and the latest open-source frameworks. You'll get a comparative tour of leading AI agent frameworks like LangChain and LangGraph, covering each tool's strengths, ideal use cases, and how to apply them in real projects. Through step-by-step examples, learn to construct single-agent and multi-agent architectures using proven design patterns to orchestrate AI agents working together. Case studies across industries show AI agents driving value in real scenarios, while guidance on responsible AI helps you implement ethical guardrails from day one. AI Agents in Practice also sets 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, 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
Master core agent components like LLMs, memory systems, tool integration, and context management
Build production-ready AI agents using frameworks like 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 forThis book is ideal for AI engineers and data scientists seeking to move beyond basic LLM implementations to building 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.Basic understanding of machine learning concepts and working knowledge of Python is required to get the best from this book & implement production-ready AI agent systems.
Contents
Table of Contents
Evolution of AI workflows since November 2022
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
What happens if we put multiple AI Agents into the same room?
Responsible AI
Conclusion
