Create LLM-powered autonomous agents and intelligent assistants tailored to your business and personal needs.From script-free customer service chatbots to fully independent agents operating seamlessly in the background, AI-powered assistants represent a breakthrough in machine intelligence. In AI Agents in Action, you'll master a proven framework for developing practical agents that handle real-world business and personal tasks.Author Micheal Lanham combines cutting-edge academic research with hands-on experience to help you: Understand and implement AI agent behavior patterns Design and deploy production-ready intelligent agents Leverage the OpenAI Assistants API and complementary tools Implement robust knowledge management and memory systems Create self-improving agents with feedback loops Orchestrate collaborative multi-agent systems Enhance agents with speech and vision capabilities You won't find toy examples or fragile assistants that require constant supervision. AI Agents in Action teaches you to build trustworthy AI capable of handling high-stakes negotiations. You'll master prompt engineering to create agents with distinct personas and profiles, and develop multi-agent collaborations that thrive in unpredictable environments. Beyond just learning a new technology, you'll discover a transformative approach to problem-solving.About the technologyMost production AI systems require many orchestrated interactions between the user, AI models, and a wide variety of data sources. AI agents capture and organize these interactions into autonomous components that can process information, make decisions, and learn from interactions behind the scenes. This book will show you how to create AI agents and connect them together into powerful multi-agent systems.About the bookIn AI Agents in Action, you'll learn how to build production-ready assistants, multi-agent systems, and behavioral agents. You'll master the essential parts of an agent, including retrieval-augmented knowledge and memory, while you create multi-agent applications that can use software tools, plan tasks autonomously, and learn from experience. As you explore the many interesting examples, you'll work with state-of-the-art tools like OpenAI Assistants API, GPT Nexus, LangChain, Prompt Flow, AutoGen, and CrewAI.What's inside Knowledge management and memory systems Feedback loops for continuous agent learning Collaborative multi-agent systems Speech and computer vision About the readerFor intermediate Python programmers.About the authorMicheal Lanham is a software and technology innovator with over 20 years of industry experience. He has authored books on deep learning, including Manning's Evolutionary Deep Learning.Table of Contents1 Introduction to agents and their world2 Harnessing the power of large language models3 Engaging GPT assistants4 Exploring multi-agent systems5 Empowering agents with actions6 Building autonomous assistants7 Assembling and using an agent platform8 Understanding agent memory and knowledge9 Mastering agent prompts with prompt flow10 Agent reasoning and evaluation11 Agent planning and feedbackA Accessing OpenAI large language modelsB Python development environment
Create LLM-powered autonomous agents and intelligent assistants tailored to your business and personal needs.From script-free customer service chatbots to fully independent agents operating seamlessly in the background, AI-powered assistants represent a breakthrough in machine intelligence. In AI Agents in Action, you'll master a proven framework for developing practical agents that handle real-world business and personal tasks.Author Micheal Lanham combines cutting-edge academic research with hands-on experience to help you: Understand and implement AI agent behavior patterns Design and deploy production-ready intelligent agents Leverage the OpenAI Assistants API and complementary tools Implement robust knowledge management and memory systems Create self-improving agents with feedback loops Orchestrate collaborative multi-agent systems Enhance agents with speech and vision capabilities You won't find toy examples or fragile assistants that require constant supervision. AI Agents in Action teaches you to build trustworthy AI capable of handling high-stakes negotiations. You'll master prompt engineering to create agents with distinct personas and profiles, and develop multi-agent collaborations that thrive in unpredictable environments. Beyond just learning a new technology, you'll discover a transformative approach to problem-solving.About the technologyMost production AI systems require many orchestrated interactions between the user, AI models, and a wide variety of data sources. AI agents capture and organize these interactions into autonomous components that can process information, make decisions, and learn from interactions behind the scenes. This book will show you how to create AI agents and connect them together into powerful multi-agent systems.About the bookIn AI Agents in Action, you'll learn how to build production-ready assistants, multi-agent systems, and behavioral agents. You'll master the essential parts of an agent, including retrieval-augmented knowledge and memory, while you create multi-agent applications that can use software tools, plan tasks autonomously, and learn from experience. As you explore the many interesting examples, you'll work with state-of-the-art tools like OpenAI Assistants API, GPT Nexus, LangChain, Prompt Flow, AutoGen, and CrewAI.What's inside Knowledge management and memory systems Feedback loops for continuous agent learning Collaborative multi-agent systems Speech and computer vision About the readerFor intermediate Python programmers.About the authorMicheal Lanham is a software and technology innovator with over 20 years of industry experience. He has authored books on deep learning, including Manning's Evolutionary Deep Learning.Table of Contents1 Introduction to agents and their world2 Harnessing the power of large language models3 Engaging GPT assistants4 Exploring multi-agent systems5 Empowering agents with actions6 Building autonomous assistants7 Assembling and using an agent platform8 Understanding agent memory and knowledge9 Mastering agent prompts with prompt flow10 Agent reasoning and evaluation11 Agent planning and feedbackA Accessing OpenAI large language modelsB Python development environment
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