Large Language Model Agents
Design, Architecture, and Practical Implementation
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Beschreibung
What if you could learn large‑model agents not by theory alone, but by building real, working systems from day one? This book offers exactly that: a practice‑driven, code‑grounded pathway into the rapidly evolving world of intelligent agents powered by large language models (LLM agents).
Designed for AI researchers, practitioners, and postgraduate students, the book reveals why large‑model agents matter now—and how their architectures, memory mechanisms, retrieval‑augmented generation (RAG), tool‑use strategies, and reasoning enhancements come together to form deployable intelligent systems. Rather than repeating definitions, it highlights the real challenges professionals face: How do you design an agent workflow that remains robust under noisy inputs? How do you debug a failing reasoning chain? How can an agent migrate smoothly across models or platforms? Rather than prescribing a single “best” solution, the book deliberately preserves open design questions and comparative experiments, encouraging readers to develop their own engineering judgment.
Across four parts—agent foundations, agent architecture, agent fine‑tuning, and frontier topics such as multimodal agents, multi‑agent systems, agent safety, and agent protocols—this book blends conceptual clarity with executable Python examples and reproducible notebooks. Readers will learn prompt engineering techniques, memory and retrieval strategies, instruction tuning, LoRA and quantization workflows, reinforcement fine‑tuning , and practical evaluation and debugging methods.
With only basic Python knowledge, readers can follow the “examples first, principles underneath” approach to master the essential skills for building, evaluating, and optimizing LLM agents. By the end, they will possess a complete methodological toolkit for stepping confidently into cutting‑edge agent research and real‑world applications.
What if you could learn large‑model agents not by theory alone, but by building real, working systems from day one? This book offers exactly that: a practice‑driven, code‑grounded pathway into the rapidly evolving world of intelligent agents powered by large language models (LLM agents).
Designed for AI researchers, practitioners, and postgraduate students, the book reveals why large‑model agents matter now—and how their architectures, memory mechanisms, retrieval‑augmented generation (RAG), tool‑use strategies, and reasoning enhancements come together to form deployable intelligent systems. Rather than repeating definitions, it highlights the real challenges professionals face: How do you design an agent workflow that remains robust under noisy inputs? How do you debug a failing reasoning chain? How can an agent migrate smoothly across models or platforms? Rather than prescribing a single “best” solution, the book deliberately preserves open design questions and comparative experiments, encouraging readers to develop their own engineering judgment.
Across four parts—agent foundations, agent architecture, agent fine‑tuning, and frontier topics such as multimodal agents, multi‑agent systems, agent safety, and agent protocols—this book blends conceptual clarity with executable Python examples and reproducible notebooks. Readers will learn prompt engineering techniques, memory and retrieval strategies, instruction tuning, LoRA and quantization workflows, reinforcement fine‑tuning , and practical evaluation and debugging methods.
With only basic Python knowledge, readers can follow the “examples first, principles underneath” approach to master the essential skills for building, evaluating, and optimizing LLM agents. By the end, they will possess a complete methodological toolkit for stepping confidently into cutting‑edge agent research and real‑world applications.
Offers a thorough review of AI agents, prompt engineering, architecture design, tool invocation, fine-tuning Provides Python code and video sources, allowing readers to practice and apply the concepts learned Balancing theoretical depth and engineering implementation, meeting the dual needs of scientific and industrial readers
Autor*in
Muning Wen
Themen in »Large Language Model Agents«
Agent Large Language Models (LLMs) Prompt Engineering Agent Fine-Tuning Retrieval-Augmented Generation Multimodal Agent Safety