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{ "item_title" : "Production-Ready Agentic AI Using MCP", "item_author" : [" Ashish Kumar "], "item_description" : "Agentic AI is no longer a research concept - it is the production frontier. If you are an AI architect, ML engineer, or cloud practitioner who needs to move beyond prototypes and deploy autonomous, multi-step AI systems that are robust, secure, and enterprise-grade, this is the book you have been waiting for.Who This Book Is For:Written for senior engineers, cloud architects, AI practitioners, and technical leads, Production-Ready Agentic AI Using MCP delivers the battle-tested patterns, reference architectures, and working code you need to build agentic AI systems that hold up in regulated, high-stakes environments - including banking, insurance, and healthcare.What's Inside:Across 12 comprehensive chapters, 43+ annotated code listings, 8 architecture diagrams, and 28 expert deep-dive sections, you will master:- Model Context Protocol (MCP) - the open standard for connecting AI agents to tools, memory, and APIs at scale- AWS Bedrock & Amazon Agents - production deployment patterns with IAM, VPC, and Guardrails- Azure OpenAI Service - enterprise integration with Managed Identity, Private Endpoints, and Responsible AI controls- Kubernetes Orchestration - deploying agentic workloads with Helm, Horizontal Pod Autoscaler, and KEDA- Zero Trust Security - end-to-end security architecture for agentic systems in regulated industries- Observability & Tracing - OpenTelemetry, distributed tracing, and LLM-specific metrics for production monitoring- Memory & State Management - short-term context windows, long-term vector stores, and hybrid retrieval strategies- Governance & Compliance - auditability, explainability, and policy enforcement for AI in regulated sectors- Multi-Agent Orchestration - agent-to-agent communication, task delegation, and failure recovery patterns- LLM Tool Calling in Production - structured outputs, schema validation, retry logic, and cost managementWhy This Book Is Different:Unlike tutorials that stop at Hello, Agent, this Expert Edition addresses the hard problems: latency under load, token cost at scale, security in zero-trust environments, compliance audit trails, and failure modes that only emerge in production. Every chapter includes production-ready code, real-world architecture decisions, and hard-won expert insights drawn from enterprise deployments.Call to Action:If you are building the next generation of enterprise AI systems, production-grade agentic AI is not optional - it is your competitive advantage. Get your copy today and start building AI that works in the real world.", "item_img_path" : "https://covers2.booksamillion.com/covers/bam/9/79/818/752/9798187529513_b.jpg", "price_data" : { "retail_price" : "39.99", "online_price" : "39.99", "our_price" : "39.99", "club_price" : "39.99", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
Production-Ready Agentic AI Using MCP|Ashish Kumar

Production-Ready Agentic AI Using MCP : A Definitive Field Guide to Designing, Deploying, and Operating Scalable Multi-Agent AI Systems on AWS and Azur

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Overview

Agentic AI is no longer a research concept - it is the production frontier. If you are an AI architect, ML engineer, or cloud practitioner who needs to move beyond prototypes and deploy autonomous, multi-step AI systems that are robust, secure, and enterprise-grade, this is the book you have been waiting for.
Who This Book Is For:
Written for senior engineers, cloud architects, AI practitioners, and technical leads, Production-Ready Agentic AI Using MCP delivers the battle-tested patterns, reference architectures, and working code you need to build agentic AI systems that hold up in regulated, high-stakes environments - including banking, insurance, and healthcare.
What's Inside:
Across 12 comprehensive chapters, 43+ annotated code listings, 8 architecture diagrams, and 28 expert deep-dive sections, you will master:
- Model Context Protocol (MCP) - the open standard for connecting AI agents to tools, memory, and APIs at scale
- AWS Bedrock & Amazon Agents - production deployment patterns with IAM, VPC, and Guardrails
- Azure OpenAI Service - enterprise integration with Managed Identity, Private Endpoints, and Responsible AI controls
- Kubernetes Orchestration - deploying agentic workloads with Helm, Horizontal Pod Autoscaler, and KEDA
- Zero Trust Security - end-to-end security architecture for agentic systems in regulated industries
- Observability & Tracing - OpenTelemetry, distributed tracing, and LLM-specific metrics for production monitoring
- Memory & State Management - short-term context windows, long-term vector stores, and hybrid retrieval strategies
- Governance & Compliance - auditability, explainability, and policy enforcement for AI in regulated sectors
- Multi-Agent Orchestration - agent-to-agent communication, task delegation, and failure recovery patterns
- LLM Tool Calling in Production - structured outputs, schema validation, retry logic, and cost management
Why This Book Is Different:
Unlike tutorials that stop at "Hello, Agent," this Expert Edition addresses the hard problems: latency under load, token cost at scale, security in zero-trust environments, compliance audit trails, and failure modes that only emerge in production. Every chapter includes production-ready code, real-world architecture decisions, and hard-won expert insights drawn from enterprise deployments.
Call to Action:
If you are building the next generation of enterprise AI systems, production-grade agentic AI is not optional - it is your competitive advantage. Get your copy today and start building AI that works in the real world.

This item is Non-Returnable

Details

  • ISBN-13: 9798187529513
  • ISBN-10: 9798187529513
  • Publisher: Independently Published
  • Publish Date: October 2026
  • Dimensions: 9 x 6 x 0.76 inches
  • Shipping Weight: 1.08 pounds
  • Page Count: 366

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