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SKU/Artículo: AMZ-1806662272
Model Context Protocol for LLMs: Build secure, scalable, and context-aware AI agents using a standardized protocol
Format:
Paperback
Detalles del producto
Disponibilidad:
En stock
En stock
Peso con empaque:
1.11 kg
1.11 kg
Devolución:
Sí
Sí
Condición
Nuevo
Nuevo
Producto de:
Amazon
Amazon
Viaja desde
USA
USA
Sobre este producto
- Learn to build composable and scalable LLM systems with the Model Context Protocol. Create context-rich, multi-agent AI apps with memory, orchestration, governance, and seamless LangChain or AutoGen integration. Key FeaturesBuild context-aware LLM systems using Model Context ProtocolIntegrate resource providers, tool agents, and context gatewaysSecure multi-agent orchestration across modular AI architecturesOptimize performance with caching, async tasks, and profilingConnect MCP with Lang Chain, Auto Gen, and RAG frameworkBook DescriptionAI developers face a growing challenge: building intelligent systems that retain long-term memory, reason over dynamic context, and integrate safely with external tools. Model Context Protocol for LLMs provides a modern solution—offering an open, modular architecture to construct scalable LLM agents with structured context exchange. This book equips you with a complete hands-on journey to MCP. You’ll implement the protocol’s key components—resource providers, tool providers, and gateways—then use these to orchestrate agents, chain workflows, and add context-aware behavior. You’ll also learn how MCP integrates seamlessly with LangChain, AutoGen, RAG systems, and multimodal applications. Security and governance are covered in depth, helping you build privacy-compliant, threat-resistant AI apps. You’ll explore caching, async tasks, load balancing, and scaling strategies for real-world readiness. With a continuous hands-on project, MCP becomes more than a standard—it becomes a blueprint for production-grade LLM development. What you will learnUnderstand why disconnected agents fail and how MCP solves itDesign standardized, context-aware interfaces with MCPImplement MCP components with Python and cloud-native toolsBuild LangChain and AutoGen workflows powered by MCPCreate scalable multi-agent systems that collaborate in real timeSecure agent interactions using authentication and access controlOptimize performance across client and server MCP deploymentsApply MCP to personalization, RAG, and multimodal AI systemsWho this book is forAI/ML engineers, solution architects, MLOps and DevOps engineers, technical product managers, and data scientists who want to build real-world multi-agent systems with secure, standardized context management. Familiarity with Python, LLMs, and basic system design is recommended.Table of ContentsIntroduction to Model Context Protocol Building a Basic Agent with State and Deployment FlowMCP for Non-Technical Readers WorkflowsMCP Components and InterfacesMCP Architecture OverviewServer-Side ImplementationClient-Side IntegrationMCP Security Model MCP Performance Optimization MCP and Multi-Agent SystemsMCP for Retrieval-Augmented GenerationMCP and LangChain IntegrationMCP and AutoGen IntegrationMCP for Enterprise Knowledge ManagementMCP for Personalization and Recommendation SystemsMCP for Multimodal ApplicationsEnterprise Knowledge ManagementCase Studies and ApplicationsEthical Considerations and Responsible AI with MCPAdvanced Topics and Future Directions
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