Reseña del libro "Production-Ready Agentic AI Using MCP (en Inglés)"
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.
