商品描述
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. Purchase of the print book includes a free eBook in PDF and ePub formats from Manning Publications. 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
- 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. Purchase of the print book includes a free eBook in PDF and ePub formats from Manning Publications. 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
商品描述(中文翻譯)
創建基於大型語言模型的自主代理和智能助手，以滿足您的商業和個人需求。
從無需腳本的客戶服務聊天機器人到在背景中無縫運行的完全獨立代理，基於人工智慧的助手代表了機器智能的一次突破。在 AI Agents in Action 中，您將掌握一個經過驗證的框架，用於開發能夠處理現實世界商業和個人任務的實用代理。 作者 Micheal Lanham 將尖端的學術研究與實踐經驗相結合，幫助您： - 理解和實施 AI 代理行為模式- 設計和部署生產就緒的智能代理
- 利用 OpenAI Assistants API 和補充工具
- 實施穩健的知識管理和記憶系統
- 創建具有反饋循環的自我改進代理
- 組織協作的多代理系統
- 增強代理的語音和視覺能力 您不會找到需要不斷監督的玩具範例或脆弱的助手。AI Agents in Action 教您構建值得信賴的 AI，能夠處理高風險的談判。您將掌握提示工程，創建具有獨特個性和檔案的代理，並發展在不可預測環境中蓬勃發展的多代理協作。除了學習新技術，您還將發現一種變革性的問題解決方法。 購買印刷書籍包括來自 Manning Publications 的免費 PDF 和 ePub 格式電子書。 關於技術 大多數生產 AI 系統需要用戶、AI 模型和各種數據源之間進行多次協調互動。AI 代理捕捉並組織這些互動，形成可以處理信息、做出決策並從幕後互動中學習的自主組件。本書將向您展示如何創建 AI 代理並將它們連接成強大的多代理系統。 關於本書 在 AI Agents in Action 中，您將學習如何構建生產就緒的助手、多代理系統和行為代理。您將掌握代理的基本部分，包括增強檢索的知識和記憶，同時創建可以使用軟體工具、自主規劃任務並從經驗中學習的多代理應用。當您探索許多有趣的範例時，您將使用尖端工具，如 OpenAI Assistants API、GPT Nexus、LangChain、Prompt Flow、AutoGen 和 CrewAI。 內容概覽 - 知識管理和記憶系統
- 持續學習的反饋循環
- 協作的多代理系統
- 語音和計算機視覺 讀者對象 針對中級 Python 程式設計師。 關於作者 Micheal Lanham 是一位擁有超過 20 年行業經驗的軟體和技術創新者。他曾撰寫有關深度學習的書籍，包括 Manning 的《Evolutionary Deep Learning》。 目錄 1 代理及其世界簡介
2 利用大型語言模型的力量
3 互動 GPT 助手
4 探索多代理系統
5 賦予代理行動能力
6 構建自主助手
7 組裝和使用代理平台
8 理解代理的記憶和知識
9 精通代理提示與提示流
10 代理推理和評估
11 代理規劃和反饋
A 訪問 OpenAI 大型語言模型
B Python 開發環境
作者簡介
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.
作者簡介(中文翻譯)
Micheal Lanham 是一位經驗豐富的軟體和科技創新者，擁有超過 20 年的經驗。他在遊戲、圖形、網頁、桌面、工程、人工智慧、地理資訊系統 (GIS) 和機器學習應用等多個領域開發了廣泛的軟體應用，服務於各種產業。在千禧年之際，Micheal 開始在遊戲開發中使用神經網路和進化演算法。
