Model Context Protocol Llms by Naveen Krishnan (15 results)

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Paperback or Softback. Condition: New. Model Context Protocol for LLMs: Build secure, scalable, and context-aware AI agents using a standardized protocol. Book.

Condition: New.

Paperback. Condition: New.

Paperback. Condition: new. Paperback. Build scalable, secure LLM applications with the Model Context Protocol and design modular, context-aware multi-agent systems for real-world deploymentFree with your book: DRM-free PDF version + access to Packt's next-gen Reader*Key FeaturesBuild modular, production-ready AI agents using the Model Context Protocol (MCP)Integrate MCP with LangChain, AutoGen, and RAG for multi-agent collaborationApply security, performance optimization, and evaluation patterns for real-world deploymentBook DescriptionModern LLM applications often fail due to weak context management, fragile tool integration, and poorly coordinated agents. To address these challenges, this book provides a practical blueprint for building reliable, scalable AI systems using the Model Context Protocol (MCP), an open standard for interoperable AI architectures.You'll explore why context is the missing layer in many AI deployments and how MCP formalizes it. Through clear explanations and practical examples, you'll design modular components such as resource providers, tool providers, gateways, and standardized interfaces. You'll also integrate MCP with LangChain, AutoGen, and RAG pipelines to build collaborative, context-aware multi-agent systems.You'll learn how to apply MCP to multimodal applications, personalization engines, and enterprise knowledge management solutions, while evaluating and benchmarking implementations for production readiness and implementing authentication, authorization, and scaling strategies for secure cloud deployments.Written by a data and AI solutions engineer with over 17 years of experience at Microsoft and Fortune 500 organizations, this guide combines architectural depth with hands-on implementation. By the end, you'll be able to design, build, and deploy secure, reusable MCP-based LLM systems that scale confidently in production.*Email sign-up and proof of purchase requiredWhat you will learnUnderstand the MCP architecture and standardized primitivesImplement resource and tool providers in PythonConnect LangChain and AutoGen to MCP pipelinesSecure agent interactions using authentication and access controlAdd RAG pipelines with shared contextual memoryApply authentication, TLS, and access control modelsOptimize performance with caching and async patternsEvaluate and benchmark MCP systems for production readinessWho this book is forAI/ML engineers, software engineers, and solution architects building LLM-powered applications in production will benefit the most from this book. Cloud architects and platform engineers designing AI infrastructure will also find it valuable. If youre looking for a standardized, modular, and secure approach to managing context across agents and tools, this guide is for you. Intermediate Python skills, a working knowledge of LLM concepts and REST APIs, and familiarity with system design patterns are expected. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

PAP. Condition: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condition: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Paperback / softback. Condition: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days.

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Paperback. Condition: new. Paperback. Build scalable, secure LLM applications with the Model Context Protocol and design modular, context-aware multi-agent systems for real-world deploymentFree with your book: DRM-free PDF version + access to Packt's next-gen Reader*Key FeaturesBuild modular, production-ready AI agents using the Model Context Protocol (MCP)Integrate MCP with LangChain, AutoGen, and RAG for multi-agent collaborationApply security, performance optimization, and evaluation patterns for real-world deploymentBook DescriptionModern LLM applications often fail due to weak context management, fragile tool integration, and poorly coordinated agents. To address these challenges, this book provides a practical blueprint for building reliable, scalable AI systems using the Model Context Protocol (MCP), an open standard for interoperable AI architectures.You'll explore why context is the missing layer in many AI deployments and how MCP formalizes it. Through clear explanations and practical examples, you'll design modular components such as resource providers, tool providers, gateways, and standardized interfaces. You'll also integrate MCP with LangChain, AutoGen, and RAG pipelines to build collaborative, context-aware multi-agent systems.You'll learn how to apply MCP to multimodal applications, personalization engines, and enterprise knowledge management solutions, while evaluating and benchmarking implementations for production readiness and implementing authentication, authorization, and scaling strategies for secure cloud deployments.Written by a data and AI solutions engineer with over 17 years of experience at Microsoft and Fortune 500 organizations, this guide combines architectural depth with hands-on implementation. By the end, you'll be able to design, build, and deploy secure, reusable MCP-based LLM systems that scale confidently in production.*Email sign-up and proof of purchase requiredWhat you will learnUnderstand the MCP architecture and standardized primitivesImplement resource and tool providers in PythonConnect LangChain and AutoGen to MCP pipelinesSecure agent interactions using authentication and access controlAdd RAG pipelines with shared contextual memoryApply authentication, TLS, and access control modelsOptimize performance with caching and async patternsEvaluate and benchmark MCP systems for production readinessWho this book is forAI/ML engineers, software engineers, and solution architects building LLM-powered applications in production will benefit the most from this book. Cloud architects and platform engineers designing AI infrastructure will also find it valuable. If youre looking for a standardized, modular, and secure approach to managing context across agents and tools, this guide is for you. Intermediate Python skills, a working knowledge of LLM concepts and REST APIs, and familiarity with system design patterns are expected. 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. Condition: new. Paperback. Build scalable, secure LLM applications with the Model Context Protocol and design modular, context-aware multi-agent systems for real-world deploymentFree with your book: DRM-free PDF version + access to Packt's next-gen Reader*Key FeaturesBuild modular, production-ready AI agents using the Model Context Protocol (MCP)Integrate MCP with LangChain, AutoGen, and RAG for multi-agent collaborationApply security, performance optimization, and evaluation patterns for real-world deploymentBook DescriptionModern LLM applications often fail due to weak context management, fragile tool integration, and poorly coordinated agents. To address these challenges, this book provides a practical blueprint for building reliable, scalable AI systems using the Model Context Protocol (MCP), an open standard for interoperable AI architectures.You'll explore why context is the missing layer in many AI deployments and how MCP formalizes it. Through clear explanations and practical examples, you'll design modular components such as resource providers, tool providers, gateways, and standardized interfaces. You'll also integrate MCP with LangChain, AutoGen, and RAG pipelines to build collaborative, context-aware multi-agent systems.You'll learn how to apply MCP to multimodal applications, personalization engines, and enterprise knowledge management solutions, while evaluating and benchmarking implementations for production readiness and implementing authentication, authorization, and scaling strategies for secure cloud deployments.Written by a data and AI solutions engineer with over 17 years of experience at Microsoft and Fortune 500 organizations, this guide combines architectural depth with hands-on implementation. By the end, you'll be able to design, build, and deploy secure, reusable MCP-based LLM systems that scale confidently in production.*Email sign-up and proof of purchase requiredWhat you will learnUnderstand the MCP architecture and standardized primitivesImplement resource and tool providers in PythonConnect LangChain and AutoGen to MCP pipelinesSecure agent interactions using authentication and access controlAdd RAG pipelines with shared contextual memoryApply authentication, TLS, and access control modelsOptimize performance with caching and async patternsEvaluate and benchmark MCP systems for production readinessWho this book is forAI/ML engineers, software engineers, and solution architects building LLM-powered applications in production will benefit the most from this book. Cloud architects and platform engineers designing AI infrastructure will also find it valuable. If youre looking for a standardized, modular, and secure approach to managing context across agents and tools, this guide is for you. Intermediate Python skills, a working knowledge of LLM concepts and REST APIs, and familiarity with system design patterns are expected. 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.

Taschenbuch. Condition: Neu. Model Context Protocol for LLMs | Build secure, scalable, and context-aware AI agents using a standardized protocol | Naveen Krishnan | Taschenbuch | Englisch | 2026 | Packt Publishing | EAN 9781806662272 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.

Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering.