{"product_id":"154415","title":"Master AI agents from theory to practice. ","description":"\u003ccenter\u003e\u003cdiv style=\"text-align:center\"\u003e\u003cimg src=\"https:\/\/tmgdisk01.cafe24.com\/images\/vs\/4172\/sv\/3jYDPLSVUVBrZER055DDf3nXZTqPlP.png?v=1765080016\" style=\"max-width:100%;max-height:10px\"\u003e\u003c\/div\u003e\u003c\/center\u003e\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\u003ccenter\u003e\n\n\u003cdiv style=\"width:95%\"\u003e\n\n\u003cdiv style=\"text-align:center;font-size:30px;font-weight:bolder;line-height:1.6em\"\u003e Master AI agents from theory to practice. \u003c\/div\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cdiv style=\"border-bottom:1px;border-bottom-style:dotted;border-color:;padding-bottom:20px\"\u003e\u003ccenter\u003e\u003ctable align=\"center\" width=\"100%\"\u003e\u003ctbody style=\"border:0px\"\u003e\n\n\u003ctr\u003e\u003ctd align=\"center\" style=\"line-height:1.2em;text-align:center;font-size:18px;color:black;font-weight:bold;padding-bottom:20px;\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\n\u003ctr\u003e\u003ctd style=\"text-align:center\"\u003e\u003cimg src=\"https:\/\/image.yes24.com\/goods\/167253365\/XL\" style=\"max-width:100%;height:auto\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\n\n\u003c\/tbody\u003e\u003c\/table\u003e\u003c\/center\u003e\u003c\/div\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cdiv style=\"width:95%;{split_style6}padding-top:20px;padding-bottom:20px\"\u003e\n\n\u003cdiv style=\"text-align:left;font-size:16px;font-weight:bold;padding-bottom:20px\"\u003e Description \u003c\/div\u003e\n\n\u003cdiv style=\"text-align:left;word-break:break-all;font-size:14px;line-height:1.6em;\"\u003e\n\n\u003cdiv\u003e\u003ch5\u003e \u003cb\u003eBook Introduction\u003c\/b\u003e\n\u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e\u003cdiv\u003e \u003cb\u003eA to Z of AI Agent Development\u003c\/b\u003e\u003cbr\u003e \u003cb\u003eAll about LLM-based agent theory and practice in just one volume!\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e This book presents a systematic roadmap for the entire process of AI agent development, starting from the principles of text encoding and transformers, which are the basis of AI agents, to LLM, RAG, knowledge graphs, reinforcement learning, and building complex multi-agent systems. \u003cbr\u003eBased on numerous papers and references, the book provides an in-depth overview of core theories and provides practical project examples, including web scraping, movie recommendation, and travel planner agents, allowing readers to learn through implementation.\u003cbr\u003e For all developers who feel anxious without theory and frustrated without practice, this book will be the most reliable guide.\u003cbr\u003e\u003cbr\u003e \u003cb\u003eRecommended for these people!\u003c\/b\u003e\u003cbr\u003e ● Developers who want to build a solid foundation in the fundamental principles of AI agents (text encoding, transformers, LLM)\u003cbr\u003e ● Practitioners who want to create autonomous agents that make decisions and act on their own, beyond simple LLM API calls.\u003cbr\u003e ● Engineers who want to systematically master the core technologies of AI agents, such as RAG, knowledge graph (GraphRAG), and reinforcement learning.\u003cbr\u003e ● Senior developers and leaders who want to experience designing and deploying the entire architecture of an AI agent system. \u003cbr\u003eResearchers and graduate students who want to study the principles of the latest AI technology in depth with theses.\u003cbr\u003e\n\u003c\/div\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cul\u003e\u003cli\u003e You can preview some of the book's contents.\u003cbr\u003e \u003cspan\u003ePreview\u003c\/span\u003e\n\n\u003c\/li\u003e\u003c\/ul\u003e\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cbr\u003e\u003cdiv\u003e\u003ch5\u003e \u003cb\u003eindex\u003c\/b\u003e\n\u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e Recommendation\u003cbr\u003e Translator's Note\u003cbr\u003e Entering\u003cbr\u003e\u003cbr\u003e Part 1: AI Agent Engine: From Text to Large-Scale Language Models\u003cbr\u003e Chapter 1: Analyzing Text Data with Deep Learning\u003cbr\u003e 1. Text representation for AI\u003cbr\u003e __One-hot encoding\u003cbr\u003e __word bag\u003cbr\u003e __TF-IDF\u003cbr\u003e 2.\u003cbr\u003e Embedding, Applications, and Representation\u003cbr\u003e __word2vec\u003cbr\u003e __Concept of text similarity\u003cbr\u003e __Properties of embedding\u003cbr\u003e 3.\u003cbr\u003e RNN, LSTM, GRU, and CNN for text processing\u003cbr\u003e __Recurrent Neural Network\u003cbr\u003e __long and short-term memory\u003cbr\u003e __gate circulation unit\u003cbr\u003e __CNN for text\u003cbr\u003e 4.\u003cbr\u003e Sentiment Analysis Using Embedding and Deep Learning\u003cbr\u003e\u003cbr\u003e Chapter 2: Transformers: The Model Behind the Modern AI Revolution\u003cbr\u003e 1.\u003cbr\u003e Exploring Attention and Self-Attention\u003cbr\u003e 2.\u003cbr\u003e Introducing the Transformer Model\u003cbr\u003e 3.\u003cbr\u003e Learning Transformers\u003cbr\u003e 4.\u003cbr\u003e Exploring Masked Language Modeling\u003cbr\u003e 5.\u003cbr\u003e Visualizing the internal mechanisms\u003cbr\u003e 6.\u003cbr\u003e Using Transformers\u003cbr\u003e \u003cbr\u003eChapter 3: Exploring the Powerful AI Engine: LLM\u003cbr\u003e 1. Examining the evolution of LLM\u003cbr\u003e __scaling law\u003cbr\u003e __Emergent characteristics\u003cbr\u003e __Context length\u003cbr\u003e __Expert Mix\u003cbr\u003e 2.\u003cbr\u003e Instruction tuning, fine tuning, alignment\u003cbr\u003e 3.\u003cbr\u003e Explore small and efficient LLMs\u003cbr\u003e 4.\u003cbr\u003e Exploring Multimodal Models\u003cbr\u003e 5.\u003cbr\u003e Understanding Hallucination and the Ethical and Legal Issues\u003cbr\u003e 6.\u003cbr\u003e Prompt Engineering\u003cbr\u003e\u003cbr\u003e Part 2: AI Agents and Knowledge Retrieval\u003cbr\u003e Chapter 4: Building a Web Scraping Agent with LLM\u003cbr\u003e 1.\u003cbr\u003e Understanding the Brain, Perception, and Behavior Paradigms\u003cbr\u003e __brains\u003cbr\u003e __perception\u003cbr\u003e __action\u003cbr\u003e 2. Classifying AI Agents\u003cbr\u003e 3.\u003cbr\u003e Understanding Single-Agent and Multi-Agent Systems\u003cbr\u003e 4.\u003cbr\u003e Explore the main libraries\u003cbr\u003e __LangChain\u003cbr\u003e __Haystack\u003cbr\u003e __LlamaIndex\u003cbr\u003e __Semantic Kernel\u003cbr\u003e __AutoGen\u003cbr\u003e Choosing an __LLM Agent Framework\u003cbr\u003e 5.\u003cbr\u003e Creating a ReAct agent that searches and finds information on its own\u003cbr\u003e\u003cbr\u003e Chapter 5: RAG-based agent to prevent hallucination\u003cbr\u003e 1.\u003cbr\u003e Exploring Naive RAG\u003cbr\u003e 2.\u003cbr\u003e Search, Optimize, Augment\u003cbr\u003e __Chunking strategy\u003cbr\u003e __Embedding Strategy\u003cbr\u003e __Embedding Database\u003cbr\u003e 3. \u003cbr\u003eEvaluate the output\u003cbr\u003e 4. Comparing RAG and Fine Tuning\u003cbr\u003e 5. Building a Movie Recommendation Agent Using RAG\u003cbr\u003e\u003cbr\u003e Chapter 6: Advanced RAG Techniques for Information Retrieval and Augmentation\u003cbr\u003e 1.\u003cbr\u003e Problems with Naive RAG\u003cbr\u003e 2.\u003cbr\u003e Explore the Advanced RAG Pipeline\u003cbr\u003e __Hierarchical indexing\u003cbr\u003e __Virtual Questions and HyDE\u003cbr\u003e __Context enhancement\u003cbr\u003e __Query Transformation\u003cbr\u003e __Keyword-based search and hybrid search\u003cbr\u003e __Query Routing\u003cbr\u003e __Re-ranking\u003cbr\u003e __Response Optimization\u003cbr\u003e 3.\u003cbr\u003e Integrating modular RAG with other systems\u003cbr\u003e __Training-based and non-training approaches\u003cbr\u003e 4.\u003cbr\u003e Implementing an Advanced RAG Pipeline\u003cbr\u003e 5. Understanding RAG's Scalability and Performance\u003cbr\u003e __Data scalability, storage, and preprocessing\u003cbr\u003e __Parallel processing\u003cbr\u003e __Security and Privacy\u003cbr\u003e 6.\u003cbr\u003e Unsolved Issues and Future Prospects\u003cbr\u003e\u003cbr\u003e Chapter 7: Creating a Knowledge Graph and Connecting It to an AI Agent\u003cbr\u003e 1.\u003cbr\u003e Introducing the Knowledge Graph\u003cbr\u003e __Formal definitions of graphs and knowledge graphs\u003cbr\u003e __Classification system and ontology\u003cbr\u003e 2. Building a Knowledge Graph Using LLM\u003cbr\u003e __Knowledge Creation\u003cbr\u003e Creating a knowledge graph with __LLM\u003cbr\u003e __Knowledge Assessment \u003cbr\u003e__Knowledge Refinement\u003cbr\u003e __Expand your knowledge\u003cbr\u003e __Knowledge Hosting and Distribution\u003cbr\u003e 3.\u003cbr\u003e Finding information using knowledge graphs and LLM\u003cbr\u003e __Graph-based indexing\u003cbr\u003e __Graph-based search\u003cbr\u003e __Using Graph RAG\u003cbr\u003e 4.\u003cbr\u003e Understanding Graph Inference\u003cbr\u003e __Knowledge Graph Embedding\u003cbr\u003e __graph neural network\u003cbr\u003e __LLM's knowledge graph inference\u003cbr\u003e 5.\u003cbr\u003e Challenges of Knowledge Graphs and Graph RAGs\u003cbr\u003e\u003cbr\u003e Chapter 8: Reinforcement Learning and AI Agents\u003cbr\u003e 1.\u003cbr\u003e Introduction to Reinforcement Learning\u003cbr\u003e __Multi-armed bandit problem\u003cbr\u003e Markov decision process\u003cbr\u003e 2.\u003cbr\u003e Deep Reinforcement Learning\u003cbr\u003e __Model-free and model-based approaches\u003cbr\u003e __On-policy and off-policy methods\u003cbr\u003e __A Closer Look at Deep Reinforcement Learning\u003cbr\u003e __Challenges and Future Prospects of Deep Reinforcement Learning\u003cbr\u003e Learning Video Games with Reinforcement Learning\u003cbr\u003e 3. Interaction between LLM and reinforcement learning models\u003cbr\u003e __LLM enhanced with reinforcement learning\u003cbr\u003e __Reinforcement Learning Enhanced with LLM\u003cbr\u003e 4.\u003cbr\u003e Key Summary\u003cbr\u003e\u003cbr\u003e Part 3: Advanced AI Agents Solving Complex Scenarios\u003cbr\u003e Chapter 9: Building Single- and Multi-Agent Systems\u003cbr\u003e 1.\u003cbr\u003e Introducing Autonomous Agents\u003cbr\u003e __Toolformer \u003cbr\u003e__Hugging GPT\u003cbr\u003e __Chemcrow\u003cbr\u003e __Swift City\u003cbr\u003e __Chem Agent\u003cbr\u003e __Multiple Agents in the Legal Field\u003cbr\u003e __Multiple agents in healthcare\u003cbr\u003e 2.\u003cbr\u003e Using Hugging GPT\u003cbr\u003e __Using Hugging GPT locally\u003cbr\u003e __Using Hugging GPT on the Web\u003cbr\u003e 3.\u003cbr\u003e multi-agent system\u003cbr\u003e 4.\u003cbr\u003e SaaS, MaaS, DaaS, RaaS\u003cbr\u003e __Software as a Service, SaaS\u003cbr\u003e __Service-as-a-Service Model, MaaS\u003cbr\u003e __Data as a Service, DaaS\u003cbr\u003e __Results as a Service, RaaS\u003cbr\u003e __Comparing various paradigms\u003cbr\u003e\u003cbr\u003e Chapter 10: Building AI Agent Applications\u003cbr\u003e 1.\u003cbr\u003e Introducing Streamlit\u003cbr\u003e __Starting Streamlet\u003cbr\u003e __Caching results\u003cbr\u003e 2.\u003cbr\u003e Front-end development with Streamlet\u003cbr\u003e __Add a text element\u003cbr\u003e Inserting an image into the Streamlet app\u003cbr\u003e __Building dynamic apps\u003cbr\u003e 3.\u003cbr\u003e Building Applications Using Streamlets and AI Agents\u003cbr\u003e 4.\u003cbr\u003e Machine Learning Operations and LLM Operations\u003cbr\u003e __Model Development\u003cbr\u003e __Model Training\u003cbr\u003e __Model Test\u003cbr\u003e __Inference Optimization\u003cbr\u003e __Handling errors in production\u003cbr\u003e __Considerations for Production Security\u003cbr\u003e 5.\u003cbr\u003e Asynchronous programming\u003cbr\u003e __asyncio \u003cbr\u003eAsynchronous Programming and Machine Learning\u003cbr\u003e 6.\u003cbr\u003e Docker\u003cbr\u003e __Kubernetes\u003cbr\u003e __Using Docker for Machine Learning\u003cbr\u003e\u003cbr\u003e Chapter 11: The Future to Come\u003cbr\u003e 1.\u003cbr\u003e AI agents in the medical field\u003cbr\u003e __AI Agents in Biomedical Sciences\u003cbr\u003e 2.\u003cbr\u003e AI agents in other industries\u003cbr\u003e __Physical Agent\u003cbr\u003e __LLM Agent for Gaming\u003cbr\u003e __Web Agent\u003cbr\u003e 3.\u003cbr\u003e Challenges to be solved and unresolved questions\u003cbr\u003e __Human-agent communication problems\u003cbr\u003e __Absence of clear superiority of multi-agents\u003cbr\u003e __Limits of inference\u003cbr\u003e __LLM's Creativity\u003cbr\u003e __Possibility of a mechanistic interpretation\u003cbr\u003e __The Path to General Artificial Intelligence\u003cbr\u003e __ethical issues\u003cbr\u003e\u003cbr\u003e Search\u003cbr\u003e\n\u003c\/div\u003e\n\u003cdiv\u003e\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cbr\u003e\u003cdiv\u003e\u003ch5\u003e \u003cb\u003eDetailed image\u003c\/b\u003e \u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cdiv\u003e\u003cimg src=\"https:\/\/image.yes24.com\/momo\/TopCate6177\/MidCate004\/617630319(1).jpg\" border=\"0\" alt=\"Detailed Image 1\"\u003e\u003c\/div\u003e\u003c\/div\u003e\n\u003cbr\u003e\u003cdiv\u003e\u003ch5\u003e \u003cb\u003ePublisher's Review\u003c\/b\u003e\n\u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e \u003cb\u003eFrom LLM and RAG basics to advanced knowledge graphs, reinforcement learning, and multi-agents.\u003c\/b\u003e\u003cbr\u003e \u003cb\u003eA complete guide to building AI agents, all in one book!\u003c\/b\u003e\u003cbr\u003e \u003cbr\u003eAlthough services leveraging large-scale language models (LLMs) have been on the rise recently, it's difficult to build a \"truly autonomous agent\" that can address users' complex needs simply by calling APIs.\u003cbr\u003e From the persistent problem of hallucinations to implementing intelligence that can make decisions, act, and learn on its own, many developers face daunting challenges.\u003cbr\u003e This book overcomes these limitations and presents a complete roadmap that delves into all the technical layers that make up AI agents from the ground up.\u003cbr\u003e\u003cbr\u003e ★ Systematic flow that penetrates all layers of the AI ​​agent\u003cbr\u003e · Part 1 lays a solid foundation, from text encoding, the foundation of AI, and RNN\/LSTM to the inner workings of transformers and LLMs, the heart of the modern AI revolution. \u003cbr\u003e· Part 2 delves into RAG (basic and advanced techniques), the core engine that makes LLM scale and smarter; knowledge graphs that maximize information retrieval and inference capabilities; and reinforcement learning for autonomous behavior learning.\u003cbr\u003e · Part 3 guides you through the practical process of integrating all these technologies to build autonomous single- and multi-agent systems, and deploying them as real-world applications using Streamlit and Docker.\u003cbr\u003e\u003cbr\u003e ★ Beyond simple tool use, become an architect with problem-solving skills\u003cbr\u003e In particular, the book emphasizes why each technology emerged and how it organically connects with other technologies. \u003cbr\u003eWe provide a comprehensive overview of how transformers overcome the limitations of RNNs, how RAGs solve the chronic problems of LLM, and why knowledge graphs are essential for complex inference.\u003cbr\u003e An approach grounded in this fundamental understanding will enable readers to move beyond mere library users to true AI architects, capable of diagnosing complex problems arising in practice and designing optimal solutions.\u003cbr\u003e\u003cbr\u003e While explaining core principles with extensive references and rich diagrams, we immediately connect theory to practice with working project examples, including a web scraping agent, a movie recommendation RAG agent, and a travel planner. \u003cbr\u003eFor all developers, researchers, and practitioners who want to delve into the operating principles of AI agents and proactively design future AI systems, this book will serve as a solid and clear guide.\u003cbr\u003e\u003cbr\u003e -The structure of this book-\u003cbr\u003e\u003cbr\u003e Chapter 1, 'Analyzing Text Data with Deep Learning', introduces methods for processing and expressing natural language in a format suitable for machine learning models.\u003cbr\u003e We cover a variety of text encoding techniques, from basic ones like one-hot encoding and bag of words to advanced representations like TF-IDF and word2vec.\u003cbr\u003e Next, we explore major deep learning architectures suitable for sequential data, such as RNN, LSTM, GRU, and CNN, and explain how to apply them to text classification tasks.\u003cbr\u003e By the end of this chapter, you will understand how these foundations enable modern language models like ChatGPT.\u003cbr\u003e \u003cbr\u003eChapter 2, \"Transformers: The Models Behind the Modern AI Revolution,\" introduces the attention mechanism and explains how it evolved into the Transformer architecture. It examines the limitations of early models like RNNs and LSTMs and examines how Transformers overcame them to become the foundation of modern natural language processing.\u003cbr\u003e Covering core topics such as self-attention, masked language modeling, learning techniques, and internal model visualization, it lays the foundation for understanding today's LLM through practical application cases.\u003cbr\u003e\u003cbr\u003e Chapter 3, \"Exploring LLM, a Powerful AI Engine,\" explores how large-scale training of Transformer models gave birth to today's LLM. It covers the evolution of LLM, its key capabilities, and its limitations, introducing techniques such as instruction tuning, fine-tuning, and alignment. \u003cbr\u003eWe also address key challenges such as smaller and more efficient LLM variant models, multimodal models handling multiple data types, hallucination and ethical issues, and prompt engineering.\u003cbr\u003e\u003cbr\u003e _Chapter 4, 'Building a Web Scraping Agent with LLM' introduces AI agents as a concept that extends LLM to complement its action-performing capabilities.\u003cbr\u003e This chapter explores the core characteristics of agents and the differences between single-agent and multi-agent systems.\u003cbr\u003e Next, we introduce the main libraries used to build agents and guide you step-by-step through the process of creating a web scraping agent that can actually retrieve information from the Internet.\u003cbr\u003e \u003cbr\u003eChapter 5, \"RAG-Based Agents Preventing Hallucination,\" explores how RAG overcomes the limitations of LLM, namely, old knowledge and hallucination. It also explains how LLM improves accuracy and adaptability by accessing external information through RAG embeddings and a vector database.\u003cbr\u003e We also compare RAG and fine-tuning, and present practical applications through hands-on practice of building a movie recommendation agent.\u003cbr\u003e\u003cbr\u003e Chapter 6, 'Advanced RAG Techniques for Information Retrieval and Augmentation', introduces techniques that extend the basic RAG architecture to improve performance at all stages of the pipeline, including data collection, indexing, retrieval, and generation.\u003cbr\u003e We address modular RAG, techniques for scaling the system to large datasets and user bases, and key challenges such as robustness and privacy. \u003cbr\u003eIt also highlights the current challenges and unresolved issues surrounding the future development of RAG-based systems.\u003cbr\u003e Chapter 7, 'Creating a Knowledge Graph and Connecting it to an AI Agent', covers how to structure text-based knowledge into a knowledge graph to enhance the information retrieval and reasoning capabilities of AI agents.\u003cbr\u003e all.\u003cbr\u003e This chapter introduces the GraphRAG concept, which leverages knowledge graphs to provide structured contextual data to LLMs.\u003cbr\u003e Next, we describe how to build a knowledge graph by extracting entities and relationships using LLM, graph-based query and inference techniques, and discuss the advantages and limitations of combining these approaches, as well as future directions for development.\u003cbr\u003e\u003cbr\u003e Chapter 8, \"Reinforcement Learning and AI Agents,\" explains how agents learn and adjust their behavior based on experience as they interact with a dynamic environment. \u003cbr\u003eThis chapter introduces the fundamental principles of reinforcement learning and covers how agents make decisions and improve their performance over time.\u003cbr\u003e We also demonstrate how to optimize behavior using neural networks.\u003cbr\u003e Finally, we conclude by discussing how to build more powerful AI systems by combining LLM and reinforcement learning.\u003cbr\u003e\u003cbr\u003e Chapter 9, \"Building Single- and Multi-Agent Systems,\" covers how to extend LLM with tools and other models to build autonomous agents.\u003cbr\u003e We introduce the concepts of single-agent and multi-agent systems, explain how LLM interacts with APIs and external models, and examine representative examples such as HuggingGPT.\u003cbr\u003e It also covers inter-agent coordination strategies, real-world use cases in complex domains, and new business paradigms such as SaaS, MaaS, DaaS, and RaaS.\u003cbr\u003e \u003cbr\u003eChapter 10, \"Building AI Agent Applications,\" explores the key challenges that arise when scaling and deploying AI agents into real-world applications.\u003cbr\u003e This chapter introduces Streamlit, a framework for rapidly prototyping front-end and back-end components of agent-based systems.\u003cbr\u003e It also covers key operational aspects, including asynchronous programming, containerization using Docker, and best practices for building scalable and operationally stable AI solutions.\u003cbr\u003e\u003cbr\u003e Chapter 11, “The Future to Come,” explores the transformative potential of AI agents across various industries, including healthcare. \u003cbr\u003eBuilding on the technological advancements discussed in the previous chapter, we review the technical and ethical challenges facing LLM and agent systems, and conclude by suggesting unresolved issues remaining in the development and deployment of intelligent AI agents, as well as future research and practical directions.\u003cbr\u003e\n\u003c\/div\u003e\n\u003cdiv\u003e\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cdiv style=\"width:95%;padding-top:20px;padding-bottom:20px\"\u003e\n\n\u003cdiv style=\"text-align:left;font-size:16px;font-weight:bold;padding-bottom:20px\"\u003e GOODS SPECIFICS \u003c\/div\u003e\n\n\u003cdiv style=\"text-align:left;font-size:14px;line-height:1.6em;\"\u003e\n\n\u003cdiv style=\"width:100%;margin-bottom:5px;line-height:1.6em;font-size:14px\"\u003e - \u003cstrong\u003eDate of issue:\u003c\/strong\u003e November 28, 2025\u003c\/div\u003e\n\n\u003cdiv style=\"width:100%;margin-bottom:5px;line-height:1.6em;font-size:14px\"\u003e - \u003cstrong\u003ePage count, weight, size:\u003c\/strong\u003e 560 pages | 188*257*35mm\u003c\/div\u003e\n\n\u003cdiv style=\"width:100%;margin-bottom:5px;line-height:1.6em;font-size:14px\"\u003e - \u003cstrong\u003eISBN13:\u003c\/strong\u003e 9788965404248\u003c\/div\u003e\n\n\u003cdiv style=\"width:100%;margin-bottom:5px;line-height:1.6em;font-size:14px\"\u003e - \u003cstrong\u003eISBN10:\u003c\/strong\u003e 896540424X \u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\u003ccenter\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cspan\u003e\u003c\/span\u003e\n\n\u003c\/center\u003e\n\n\n\u003c\/center\u003e","brand":"LIBRAIRIE COREENNE","offers":[{"title":"Default Title","offer_id":43893424652330,"sku":"154415","price":49.0,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0683\/2750\/5962\/files\/6c571314691979bd6281af222ee0b3bb.jpg?v=1765401469","url":"https:\/\/librairie.coreenne.fr\/en\/products\/154415","provider":"LIBRAIRIE COREENNE","version":"1.0","type":"link"}