Isbn: 9798279276400 - intelligent ai automation with large language models: building ai agents, workflow orchestration pipelines, and autonomous systems for real-world applications. (11 resultados)

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

Condición: New.

Condición: As New. Unread book in perfect condition.

Paperback. Condición: new. Paperback. Modern software is no longer just programmed, it is orchestrated.As large language models evolve from simple text generators into reasoning engines, a new engineering discipline has emerged: Intelligent Automation with Large Language Models. This book is your practical, end-to-end guide to designing automation systems that can think, plan, and act autonomously across real-world environments.Intelligent Automation with Large Language Models shows you how to move beyond isolated prompts and experiments to build AI agents, workflow orchestration pipelines, and autonomous systems that integrate seamlessly with APIs, business tools, and production infrastructure. You'll learn how to engineer intelligent workflows where LLMs don't just respond, they coordinate, reason, and make decisions within structured systems.Bridging theory and hands-on implementation, this book walks you through the core building blocks of modern AI automation: agent design, context management, workflow orchestration, memory systems, and multi-step decision pipelines. Each concept is grounded in practical architectures that can be adapted to real applications, from document processing and research automation to business operations and internal tooling.Rather than focusing on hype or fragile one-off solutions, this guide emphasizes engineering discipline: scalability, reliability, cost control, monitoring, and responsible deployment. You'll learn how to design automation systems that are not only powerful, but maintainable, observable, and production-ready.What You'll LearnCore principles of intelligent automation and LLM-driven system designHow to build and coordinate AI agents for multi-step tasksDesigning workflow orchestration pipelines that connect LLMs, APIs, and toolsContext and memory strategies for long-running and stateful AI systemsPatterns for autonomous decision-making and agent collaborationBest practices for scaling, monitoring, and governing AI automation in productionReal-world architectures for business, research, and operational automationWho This Book Is ForThis book is written for developers, engineers, data professionals, and technical builders who want to move beyond experimentation and start delivering real value with AI automation. Whether you're integrating LLMs into existing systems, designing agentic workflows, or building automation platforms from the ground up, this guide provides reproducible frameworks you can apply immediately.No hype. No shortcuts. Just clear, practical engineering for the next generation of intelligent systems. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Condición: New. Print on Demand.

Paperback. Condición: New.

Paperback. Condición: new. Paperback. Modern software is no longer just programmed, it is orchestrated.As large language models evolve from simple text generators into reasoning engines, a new engineering discipline has emerged: Intelligent Automation with Large Language Models. This book is your practical, end-to-end guide to designing automation systems that can think, plan, and act autonomously across real-world environments.Intelligent Automation with Large Language Models shows you how to move beyond isolated prompts and experiments to build AI agents, workflow orchestration pipelines, and autonomous systems that integrate seamlessly with APIs, business tools, and production infrastructure. You'll learn how to engineer intelligent workflows where LLMs don't just respond, they coordinate, reason, and make decisions within structured systems.Bridging theory and hands-on implementation, this book walks you through the core building blocks of modern AI automation: agent design, context management, workflow orchestration, memory systems, and multi-step decision pipelines. Each concept is grounded in practical architectures that can be adapted to real applications, from document processing and research automation to business operations and internal tooling.Rather than focusing on hype or fragile one-off solutions, this guide emphasizes engineering discipline: scalability, reliability, cost control, monitoring, and responsible deployment. You'll learn how to design automation systems that are not only powerful, but maintainable, observable, and production-ready.What You'll LearnCore principles of intelligent automation and LLM-driven system designHow to build and coordinate AI agents for multi-step tasksDesigning workflow orchestration pipelines that connect LLMs, APIs, and toolsContext and memory strategies for long-running and stateful AI systemsPatterns for autonomous decision-making and agent collaborationBest practices for scaling, monitoring, and governing AI automation in productionReal-world architectures for business, research, and operational automationWho This Book Is ForThis book is written for developers, engineers, data professionals, and technical builders who want to move beyond experimentation and start delivering real value with AI automation. Whether you're integrating LLMs into existing systems, designing agentic workflows, or building automation platforms from the ground up, this guide provides reproducible frameworks you can apply immediately.No hype. No shortcuts. Just clear, practical engineering for the next generation of intelligent systems. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…