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Paperback. Condizione: Fair. Revised. The item might be beaten up but readable. May contain markings or highlighting, as well as stains, bent corners, or any other major defect, but the text is not obscured in any way.

Condizione: New.

PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

Condizione: As New. Unread book in perfect condition.

Condizione: New.

PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

Paperback. Condizione: New.

Condizione: New.

Condizione: As New. Unread book in perfect condition.

Condizione: New.

Paperback. Condizione: Brand New. revised edition. 254 pages. 9.50x6.50x0.75 inches. In Stock.

Paperback. Condizione: new. Paperback. Agentic AI Systems: Architectures and Design Patterns for Autonomous IntelligenceMost developers who work with large language models hit the same wall eventually. The model is impressive. The demos work. Then something breaks in production at two in the morning and there is no architectural foundation to fall back on, because what was built was never really a system. It was a prompt chain dressed up to look like one.This book is about closing that gap. Not the gap between knowing about AI and understanding AI, but the more practical gap between having access to a capable language model and having built something that runs autonomously, handles failures gracefully, and does useful work without someone driving every step.The difference between a language model and an agent sounds simple on paper. A language model takes input, produces output, and stops. An agent has a goal. It takes steps toward that goal, uses tools to affect the world around it, monitors what happens, and adjusts when things go wrong. That architectural difference turns out to change almost everything about how you design, build, test, and operate the system.This is a practitioner's book written by someone who has written thousands of lines of LangGraph orchestration code, failed at AutoGen fine-tuning more than once, and stood in front of product teams trying to explain what "reflection" means in the context of an AI agent. The frameworks covered here - LangGraph, CrewAI, AutoGen - exist today, ship to production today, and are being used by real companies to automate real work. The research literature is valuable, but this book lives in the space between theory and deployment.The book is organized in four sections. The first covers the foundations: what an agent actually is, how memory and planning work, where the major generative model architectures fit in, and why the transformer became the engine underneath all of it. The second gets into architecture - reactive agents, deliberative agents, hybrid systems, the Coordinator-Worker-Delegator pattern, tool use, and the underappreciated complexity of state management. The third covers scale and operations: multi-agent coordination, evaluation, human-in-the-loop design, and governance. The fourth closes with applied case studies and design canvases drawn from production deployments in marketing, financial research, healthcare navigation, and DevOps automation.Throughout, the code examples are concrete and runnable, not illustrative pseudocode. The architectural principles are grounded in what actually breaks in production, not in what looks elegant on a whiteboard. And the honest parts - the places where the tooling is still immature, where the models are unreliable in specific ways, where the governance questions do not have clean answers yet - are included because pretending otherwise does not help anyone build better systems.If you are a developer who has gotten reasonably good at prompt engineering and wants to understand what comes next, this book is for you. If you are a technical lead trying to figure out what "adding AI" to a product actually requires architecturally, this book is for you. If you are building something autonomous and want a clear-eyed guide to the patterns that work and the failure modes you will encounter, this is the book.The field is moving fast. Some of what is written here will be outdated by the time you read it. What will not be outdated is why you need memory, why planning matters, why human oversight is not optional, and why an agent that cannot handle failure gracefully is not really an agent at all. Those principles hold regardless of which framework is newest. Everything else you can adapt. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Paperback. Condizione: New.

Paperback. Condizione: new. Paperback. Unlock the Next Level of Artificial Intelligence - Beyond Prompts, Toward True Autonomy.Large Language Models changed how we generate information.Now, Agentic AI is changing how we think, plan, and act.If AI is the spark, agentic systems are the fire.This book reveals how to build, design, and orchestrate autonomous AI agents capable of reasoning, memory, reflection, and multi-step decision-making.What You'll Learn Inside- How Agentic AI Works - Discover what transforms a static LLM into a goal-driven, adaptive agent.- Architectural Blueprints - Master the CWD Pattern (Coordinator-Worker-Delegator) and modular design for scalable systems.- Reactive vs Deliberative Thinking - Learn how hybrid cognition merges speed with strategy.- Memory & Feedback Loops - Implement persistence, context retention, and continuous learning in your agents.- Tool Use & Real-World Action - Connect APIs, databases, and tools so your AI can do things, not just say them.- Practical Canvas Gallery - Explore 12+ ready-to-use agent templates (AI Marketer, DevOps Architect, E-Commerce Strategist, and more).Who This Book Is ForAI builders, developers, and researchers who want to move from prompt-engineering to real autonomy.Entrepreneurs exploring how to deploy multi-agent systems for business or productivity.Students and professionals who want to understand the next frontier of Artificial Intelligence.Inside the Book33 detailed infographics and blueprints for visual learners.Real-world agent workflows, case studies, and annotated examples.A step-by-step guide for designing systems that observe, plan, act, and reflect.Access to bonus canvas resources for hands-on experimentation.Why Readers Love Building Agentic AI Systems"Finally, a book that explains agentic intelligence in a way that's actionable.""Brilliant visuals - this is the missing link between LLMs and true AI autonomy.""It's not theory - it's a playbook for building smarter systems."The Future of AI Isn't Reactive - It's Agentic.If you want to stay ahead of the curve in AI design, this isn't just another book - it's your blueprint for the next era of intelligent systems.Get your copy of Building Agentic AI Systems today and start designing smarter, autonomous AI agents. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Single Issue Magazine. Condizione: Good. First Edition. 94 pages. Fiction: The Terrible Morning; Mountain Time; (part 4 of 5); The Pond; A Serious Study of Love; A Man Needs a Horse; The Importance of Being Kissed. Articles: All Out Against Cancer; Little Miss Innocent; American in Germany; Underwater Wealth; A.W.O.L. De Luxe - Private John Martin, and American in the Canadian army; Fashion on the Downgrade; Lindbergh in Battle (conclusion); The Little Doc - Doris Gnauck; Bad Neighbor Policy - our diplomatic bungling in Latin America. Ads include: Mary Brewer in Ipana ad; Perry Como and Martha Stewart in G.E. Radio ad; Great logging-theme ad for International Trucks; Mercury cars; Elsie the Cow; Piper Cub; American Airline System; REO trucks; Clark's Teaberry Gum; Perilous Holiday (Movie ad); The Lockheed Constellation aircraft; Great back cover Coke ad shows male and female troops on train. Unmarked. Average wear. A sound copy of this vintage issue.; Folio - over 12" - 15" tall; Collier's Magazine, February 23, 1946 - America in Germany Terrible Morning; Mountain Time; (part 4 of 5); The Pond; A Serious Study of Love; A Man Needs a Horse; The Importance of Being Kissed. Articles: All Out Against Cancer; Little Miss Innocent; Ame. Illustrated by McLeod, Ronald; Gillies, Bill (cover); Resnick, Saul;Gregori, Leon; Hurst, Oliver; Michael; Garland, George (illustratore). …

Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Agentic AI Systems: Architectures and Design Patterns for Autonomous IntelligenceMost developers who work with large language models hit the same wall eventually. The model is impressive. The demos work. Then something breaks in production at two in the morning and there is no architectural foundation to fall back on, because what was built was never really a system. It was a prompt chain dressed up to look like one.This book is about closing that gap. Not the gap between knowing about AI and understanding AI, but the more practical gap between having access to a capable language model and having built something that runs autonomously, handles failures gracefully, and does useful work without someone driving every step.The difference between a language model and an agent sounds simple on paper. A language model takes input, produces output, and stops. An agent has a goal. It takes steps toward that goal, uses tools to affect the world around it, monitors what happens, and adjusts when things go wrong. That architectural difference turns out to change almost everything about how you design, build, test, and operate the system.This is a practitioner's book written by someone who has written thousands of lines of LangGraph orchestration code, failed at AutoGen fine-tuning more than once, and stood in front of product teams trying to explain what 'reflection' means in the context of an AI agent. The frameworks covered here - LangGraph, CrewAI, AutoGen - exist today, ship to production today, and are being used by real companies to automate real work. The research literature is valuable, but this book lives in the space between theory and deployment.The book is organized in four sections. The first covers the foundations: what an agent actually is, how memory and planning work, where the major generative model architectures fit in, and why the transformer became the engine underneath all of it. The second gets into architecture - reactive agents, deliberative agents, hybrid systems, the Coordinator-Worker-Delegator pattern, tool use, and the underappreciated complexity of state management. The third covers scale and operations: multi-agent coordination, evaluation, human-in-the-loop design, and governance. The fourth closes with applied case studies and design canvases drawn from production deployments in marketing, financial research, healthcare navigation, and DevOps automation.Throughout, the code examples are concrete and runnable, not illustrative pseudocode. The architectural principles are grounded in what actually breaks in production, not in what looks elegant on a whiteboard. And the honest parts - the places where the tooling is still immature, where the models are unreliable in specific ways, where the governance questions do not have clean answers yet - are included because pretending otherwise does not help anyone build better systems.If you are a developer who has gotten reasonably good at prompt engineering and wants to understand what comes next, this book is for you. If you are a technical lead trying to figure out what 'adding AI' to a product actually requires architecturally, this book is for you. If you are building something autonomous and want a clear-eyed guide to the patterns that work and the failure modes you will encounter, this is the book.The field is moving fast. Some of what is written here will be outdated by the time you read it. What will not be outdated is why you need memory, why planning matters, why human oversight is not optional, and why an agent that cannot handle failure gracefully is not really an agent at all. Those principles hold regardless of which framework is newest. Everything else you can adapt. …

Paperback. Condizione: new. Paperback. Agentic AI Systems: Architectures and Design Patterns for Autonomous IntelligenceMost developers who work with large language models hit the same wall eventually. The model is impressive. The demos work. Then something breaks in production at two in the morning and there is no architectural foundation to fall back on, because what was built was never really a system. It was a prompt chain dressed up to look like one.This book is about closing that gap. Not the gap between knowing about AI and understanding AI, but the more practical gap between having access to a capable language model and having built something that runs autonomously, handles failures gracefully, and does useful work without someone driving every step.The difference between a language model and an agent sounds simple on paper. A language model takes input, produces output, and stops. An agent has a goal. It takes steps toward that goal, uses tools to affect the world around it, monitors what happens, and adjusts when things go wrong. That architectural difference turns out to change almost everything about how you design, build, test, and operate the system.This is a practitioner's book written by someone who has written thousands of lines of LangGraph orchestration code, failed at AutoGen fine-tuning more than once, and stood in front of product teams trying to explain what "reflection" means in the context of an AI agent. The frameworks covered here - LangGraph, CrewAI, AutoGen - exist today, ship to production today, and are being used by real companies to automate real work. The research literature is valuable, but this book lives in the space between theory and deployment.The book is organized in four sections. The first covers the foundations: what an agent actually is, how memory and planning work, where the major generative model architectures fit in, and why the transformer became the engine underneath all of it. The second gets into architecture - reactive agents, deliberative agents, hybrid systems, the Coordinator-Worker-Delegator pattern, tool use, and the underappreciated complexity of state management. The third covers scale and operations: multi-agent coordination, evaluation, human-in-the-loop design, and governance. The fourth closes with applied case studies and design canvases drawn from production deployments in marketing, financial research, healthcare navigation, and DevOps automation.Throughout, the code examples are concrete and runnable, not illustrative pseudocode. The architectural principles are grounded in what actually breaks in production, not in what looks elegant on a whiteboard. And the honest parts - the places where the tooling is still immature, where the models are unreliable in specific ways, where the governance questions do not have clean answers yet - are included because pretending otherwise does not help anyone build better systems.If you are a developer who has gotten reasonably good at prompt engineering and wants to understand what comes next, this book is for you. If you are a technical lead trying to figure out what "adding AI" to a product actually requires architecturally, this book is for you. If you are building something autonomous and want a clear-eyed guide to the patterns that work and the failure modes you will encounter, this is the book.The field is moving fast. Some of what is written here will be outdated by the time you read it. What will not be outdated is why you need memory, why planning matters, why human oversight is not optional, and why an agent that cannot handle failure gracefully is not really an agent at all. Those principles hold regardless of which framework is newest. Everything else you can adapt. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…

Paperback. Condizione: new. Paperback. Agentic AI Systems: Architectures and Design Patterns for Autonomous IntelligenceMost developers who work with large language models hit the same wall eventually. The model is impressive. The demos work. Then something breaks in production at two in the morning and there is no architectural foundation to fall back on, because what was built was never really a system. It was a prompt chain dressed up to look like one.This book is about closing that gap. Not the gap between knowing about AI and understanding AI, but the more practical gap between having access to a capable language model and having built something that runs autonomously, handles failures gracefully, and does useful work without someone driving every step.The difference between a language model and an agent sounds simple on paper. A language model takes input, produces output, and stops. An agent has a goal. It takes steps toward that goal, uses tools to affect the world around it, monitors what happens, and adjusts when things go wrong. That architectural difference turns out to change almost everything about how you design, build, test, and operate the system.This is a practitioner's book written by someone who has written thousands of lines of LangGraph orchestration code, failed at AutoGen fine-tuning more than once, and stood in front of product teams trying to explain what "reflection" means in the context of an AI agent. The frameworks covered here - LangGraph, CrewAI, AutoGen - exist today, ship to production today, and are being used by real companies to automate real work. The research literature is valuable, but this book lives in the space between theory and deployment.The book is organized in four sections. The first covers the foundations: what an agent actually is, how memory and planning work, where the major generative model architectures fit in, and why the transformer became the engine underneath all of it. The second gets into architecture - reactive agents, deliberative agents, hybrid systems, the Coordinator-Worker-Delegator pattern, tool use, and the underappreciated complexity of state management. The third covers scale and operations: multi-agent coordination, evaluation, human-in-the-loop design, and governance. The fourth closes with applied case studies and design canvases drawn from production deployments in marketing, financial research, healthcare navigation, and DevOps automation.Throughout, the code examples are concrete and runnable, not illustrative pseudocode. The architectural principles are grounded in what actually breaks in production, not in what looks elegant on a whiteboard. And the honest parts - the places where the tooling is still immature, where the models are unreliable in specific ways, where the governance questions do not have clean answers yet - are included because pretending otherwise does not help anyone build better systems.If you are a developer who has gotten reasonably good at prompt engineering and wants to understand what comes next, this book is for you. If you are a technical lead trying to figure out what "adding AI" to a product actually requires architecturally, this book is for you. If you are building something autonomous and want a clear-eyed guide to the patterns that work and the failure modes you will encounter, this is the book.The field is moving fast. Some of what is written here will be outdated by the time you read it. What will not be outdated is why you need memory, why planning matters, why human oversight is not optional, and why an agent that cannot handle failure gracefully is not really an agent at all. Those principles hold regardless of which framework is newest. Everything else you can adapt. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Paperback. Condizione: new. Paperback. Unlock the Next Level of Artificial Intelligence - Beyond Prompts, Toward True Autonomy.Large Language Models changed how we generate information.Now, Agentic AI is changing how we think, plan, and act.If AI is the spark, agentic systems are the fire.This book reveals how to build, design, and orchestrate autonomous AI agents capable of reasoning, memory, reflection, and multi-step decision-making.What You'll Learn Inside- How Agentic AI Works - Discover what transforms a static LLM into a goal-driven, adaptive agent.- Architectural Blueprints - Master the CWD Pattern (Coordinator-Worker-Delegator) and modular design for scalable systems.- Reactive vs Deliberative Thinking - Learn how hybrid cognition merges speed with strategy.- Memory & Feedback Loops - Implement persistence, context retention, and continuous learning in your agents.- Tool Use & Real-World Action - Connect APIs, databases, and tools so your AI can do things, not just say them.- Practical Canvas Gallery - Explore 12+ ready-to-use agent templates (AI Marketer, DevOps Architect, E-Commerce Strategist, and more).Who This Book Is ForAI builders, developers, and researchers who want to move from prompt-engineering to real autonomy.Entrepreneurs exploring how to deploy multi-agent systems for business or productivity.Students and professionals who want to understand the next frontier of Artificial Intelligence.Inside the Book33 detailed infographics and blueprints for visual learners.Real-world agent workflows, case studies, and annotated examples.A step-by-step guide for designing systems that observe, plan, act, and reflect.Access to bonus canvas resources for hands-on experimentation.Why Readers Love Building Agentic AI Systems"Finally, a book that explains agentic intelligence in a way that's actionable.""Brilliant visuals - this is the missing link between LLMs and true AI autonomy.""It's not theory - it's a playbook for building smarter systems."The Future of AI Isn't Reactive - It's Agentic.If you want to stay ahead of the curve in AI design, this isn't just another book - it's your blueprint for the next era of intelligent systems.Get your copy of Building Agentic AI Systems today and start designing smarter, autonomous AI agents. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Taschenbuch. Condizione: Neu. AGENTIC AI SYSTEMS | Oliver Tomlinson | Taschenbuch | Englisch | 2026 | Oliver Tomlinson | EAN 9791282262224 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.