關於本電子書
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 technology
Most 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 book
In 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 reader
For intermediate Python programmers.
About the author
Micheal 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 Contents
1 Introduction to agents and their world
2 Harnessing the power of large language models
3 Engaging GPT assistants
4 Exploring multi-agent systems
5 Empowering agents with actions
6 Building autonomous assistants
7 Assembling and using an agent platform
8 Understanding agent memory and knowledge
9 Mastering agent prompts with prompt flow
10 Agent reasoning and evaluation
11 Agent planning and feedback
A Accessing OpenAI large language models
B Python development environment
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 technology
Most 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 book
In 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 reader
For intermediate Python programmers.
About the author
Micheal 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 Contents
1 Introduction to agents and their world
2 Harnessing the power of large language models
3 Engaging GPT assistants
4 Exploring multi-agent systems
5 Empowering agents with actions
6 Building autonomous assistants
7 Assembling and using an agent platform
8 Understanding agent memory and knowledge
9 Mastering agent prompts with prompt flow
10 Agent reasoning and evaluation
11 Agent planning and feedback
A Accessing OpenAI large language models
B Python development environment
關於作者
Micheal Lanham is a proven software and tech innovator with over 20 years of experience. He has developed a broad range of software applications in areas such as games, graphics, web, desktop, engineering, artificial intelligence, GIS, and machine learning applications for a variety of industries. At the turn of the millennium, Micheal began working with neural networks and evolutionary algorithms in game development.
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