ISBN: 9798181010437 - production agent engineering with claude code: build autonomous agents, mcp integrations, multi-agent systems, hooks, computer use, and production-ready ai workflows (6 resultados)

Paperback. Condición: new. Paperback. This book is a practical, engineering-focused guide to building real-world autonomous agent systems using Claude Code. It moves beyond surface-level explanations and focuses on how production-grade AI systems are actually designed, structured, deployed, and scaled.Modern AI agents are no longer simple chat interfaces or scripted automations. They are becoming full execution systems capable of planning, tool use, memory management, environment interaction, and multi-agent coordination. However, most developers struggle when moving from prototypes to systems that can run reliably in production. This book bridges that gap.Inside, you will learn how to design agents that are not only intelligent, but also stable, observable, secure, and scalable. Every concept is grounded in real engineering patterns used in production AI systems, including event-driven architectures, memory pipelines, tool execution layers, and distributed agent coordination.This is not a theoretical overview. It is a hands-on engineering guide.Who This Book Is ForThis book is designed for developers and engineers who want to build real AI systems, not just experiment with prompts.AI engineers building autonomous agent systemsPython developers transitioning into AI/LLM engineeringMachine learning engineers moving into production AI systemsSoftware engineers working with LLM APIs and tool integrationsSystem architects designing scalable AI infrastructureAdvanced learners exploring multi-agent systems and automation workflowsIf you already understand basic programming and want to move into building intelligent systems that can operate autonomously in real environments, this book is written for you.What You Will LearnThroughout this book, you will learn how to design and engineer full-scale agent systems using Claude Code and modern AI architecture principles.Build autonomous agents that can reason, plan, and execute tasksDesign MCP-based integrations for structured tool and context accessImplement event-driven architectures using hooks and triggersCreate multi-agent systems that collaborate and coordinate tasksEngineer memory systems for long-term context and knowledge retentionBuild safe computer-use systems for browser and desktop interactionDesign human-in-the-loop workflows for controlled autonomyImplement monitoring, logging, and observability layersSecure agent systems using governance and access control modelsScale agent architectures for production workloads and real trafficEach concept is explained from first principles and then translated into real, working Python implementations.Why This Book MattersMost AI tutorials stop at simple API calls or basic chatbot examples. In real-world systems, that is only the beginning.Production agent systems must handle uncertainty, failure, scaling, memory constraints, and external tool dependencies. They must also remain safe, observable, and maintainable over time.This book focuses on exactly that layer of engineering.You will not only learn how to make agents "work," but how to make them reliable enough to run continuously in production environments where failure is not an option.A Practical Engineering ApproachEvery concept in this book is built around real engineering patterns: Modular system designEvent-driven execution modelsStructured memory and retrieval pipelinesAgent orchestration and delegationSafe execution boundaries and governance layersScalable distributed processing modelsCode examples are provided throughout to ensure that each idea can be implemented immedia Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Paperback. Condición: new. Paperback. This book is a practical, engineering-focused guide to building real-world autonomous agent systems using Claude Code. It moves beyond surface-level explanations and focuses on how production-grade AI systems are actually designed, structured, deployed, and scaled.Modern AI agents are no longer simple chat interfaces or scripted automations. They are becoming full execution systems capable of planning, tool use, memory management, environment interaction, and multi-agent coordination. However, most developers struggle when moving from prototypes to systems that can run reliably in production. This book bridges that gap.Inside, you will learn how to design agents that are not only intelligent, but also stable, observable, secure, and scalable. Every concept is grounded in real engineering patterns used in production AI systems, including event-driven architectures, memory pipelines, tool execution layers, and distributed agent coordination.This is not a theoretical overview. It is a hands-on engineering guide.Who This Book Is ForThis book is designed for developers and engineers who want to build real AI systems, not just experiment with prompts.AI engineers building autonomous agent systemsPython developers transitioning into AI/LLM engineeringMachine learning engineers moving into production AI systemsSoftware engineers working with LLM APIs and tool integrationsSystem architects designing scalable AI infrastructureAdvanced learners exploring multi-agent systems and automation workflowsIf you already understand basic programming and want to move into building intelligent systems that can operate autonomously in real environments, this book is written for you.What You Will LearnThroughout this book, you will learn how to design and engineer full-scale agent systems using Claude Code and modern AI architecture principles.Build autonomous agents that can reason, plan, and execute tasksDesign MCP-based integrations for structured tool and context accessImplement event-driven architectures using hooks and triggersCreate multi-agent systems that collaborate and coordinate tasksEngineer memory systems for long-term context and knowledge retentionBuild safe computer-use systems for browser and desktop interactionDesign human-in-the-loop workflows for controlled autonomyImplement monitoring, logging, and observability layersSecure agent systems using governance and access control modelsScale agent architectures for production workloads and real trafficEach concept is explained from first principles and then translated into real, working Python implementations.Why This Book MattersMost AI tutorials stop at simple API calls or basic chatbot examples. In real-world systems, that is only the beginning.Production agent systems must handle uncertainty, failure, scaling, memory constraints, and external tool dependencies. They must also remain safe, observable, and maintainable over time.This book focuses on exactly that layer of engineering.You will not only learn how to make agents "work," but how to make them reliable enough to run continuously in production environments where failure is not an option.A Practical Engineering ApproachEvery concept in this book is built around real engineering patterns: Modular system designEvent-driven execution modelsStructured memory and retrieval pipelinesAgent orchestration and delegationSafe execution boundaries and governance layersScalable distributed processing modelsCode examples are provided throughout to ensure that each idea can be implem Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

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