{"product_id":"9789819256587","title":"Agentic Large Language Models","description":"\u003ch1\u003eAgentic Large Language Models\u003c\/h1\u003e\u003ch3\u003eMax van Duijn | Michiel van der Meer | Aske Plaat | Niki van Stein\u003c\/h3\u003e\u003cdiv\u003e\u003cb\u003eComputers \/ Artificial Intelligence \/ General\u003c\/b\u003e\u003c\/div\u003e\u003cbr\u003e\u003cdiv\u003e\n\u003cp\u003eAgentic Large Language Models represent a rapidly emerging frontier in artificial intelligence, integrating generative modeling with the capacity to reason, act, and interact autonomously. This graduate‑level textbook provides the first systematic and comprehensive treatment of agentic LLMs, offering a unified framework that spans foundational principles, computational mechanisms, and advanced applications.\u003c\/p\u003e\n\u003cp\u003eThe book surveys the full methodological landscape: the architecture and training pipeline of LLMs; scaling laws; multi-step reasoning and self-reflection; multimodal vision-language-action models; world-model construction; interaction styles and memory architectures; and agentic tool-chains, including the Model Context Protocol and the robustness, safety, and security concerns that arise once agents can act in the world. It further examines behavioral and cognitive dimensions of agentic systems, including Theory of Mind, strategic behavior, emergent norms, and collective intelligence in multi-agent environments. Advanced chapters address synthetic data generation, mechanistic interpretability, and cognitive architectures, highlighting open challenges and research opportunities.\u003c\/p\u003e\n\u003cp\u003eFour hands-on case studies take readers from training a small language model from scratch, through benchmarking reasoning and self-reflection on games and puzzles, to building a multi-agent medical diagnosis system and simulating a society of interacting LLM agents. Written for graduate students and LLM developers with foundational AI knowledge and supported by Python-based examples and open code repositories, this textbook serves as both a course text and a reference for the theoretical foundations, computational models, and future directions of agentic LLM research.\u003c\/p\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003cp\u003e\u003cstrong\u003eMax van Duijn\u003c\/strong\u003e is Assistant Professor at Leiden University, specializing in the intersection of cognitive science, linguistics, and artificial intelligence. His research examines the mechanisms underlying social intelligence—empathy, perspective‑taking, and Theory of Mind—and models these capacities in both humans and AI systems, including Large Language Models. He previously edited a Springer LNCS volume on misinformation in online media.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eMichiel van der Meer\u003c\/strong\u003e is a Researcher at Leiden University whose work focuses on open, agentic LLMs that reason over human values, opinions, and arguments—engineered end-to-end across the pipeline (data, training, alignment, inference, evaluation) to faithfully represent the diversity of a society.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eAske Plaat\u003c\/strong\u003e is Full Professor of Artificial Intelligence at Leiden University. His research spans large reasoning models, machine learning, and AI‑generated music, with a particular focus on computational strategies for complex decision‑making. He is the author of two Springer textbooks on reinforcement learning—\u003cem\u003eLearning to Play: Reinforcement Learning and Games\u003c\/em\u003e (2020) and \u003cem\u003eDeep Reinforcement Learning\u003c\/em\u003e (2022)—both widely used in graduate‑level AI education.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eNiki van Stein\u003c\/strong\u003e is Associate Professor at Leiden University, where she works on explainable AI for machine learning and optimization. Her research combines interpretability with metaheuristic and evolutionary optimization, including the use of large language models for automated algorithm design and code generation. She co-edited the Springer volume\u003cem\u003e Explainable AI for Evolutionary Computation\u003c\/em\u003e and develops open-source tools for analyzing and benchmarking black-box optimization algorithms.\u003c\/p\u003e\n\u003c\/div\u003e\u003cbr\u003e\u003ctable\u003e\n\u003ctr\u003e\n\u003ctd\u003ePublication Date: \u003c\/td\u003e\n\u003ctd\u003e28 March 2027\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePublisher: \u003c\/td\u003e\n\u003ctd\u003eSpringer Nature Singapore\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eImprint: \u003c\/td\u003e\n\u003ctd\u003eSpringer\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eISBN-13: \u003c\/td\u003e\n\u003ctd\u003e9789819256587\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eFormat: \u003c\/td\u003e\n\u003ctd\u003eHardback\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e","brand":"Springer Nature Singapore","offers":[{"title":"Default Title","offer_id":52370119524492,"sku":"9789819256587","price":71.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0710\/9545\/1788\/files\/9789819256587.jpg?v=1790900871","url":"https:\/\/lateknightbooks.com\/products\/9789819256587","provider":"Late Knight Books and Services, LLC","version":"1.0","type":"link"}