Your AI agents work in staging. They break in production. Context leaks. Costs spike. Logs tell you nothing useful. Compliance teams start asking hard questions. Vendor lock-in quietly tightens its grip.

Modern AI systems are not just prompts and APIs. They are distributed, stateful, compliance-sensitive platforms that must scale under pressure. If you are building LLM-powered tools without platform-grade architecture, you are gambling with reliability, security, and long-term flexibility.

AI Platform Engineering with MCP Servers delivers a practical blueprint for building context-aware agent systems that behave predictably in real-world environments. This book shows how to design MCP server architectures that separate model logic from orchestration, enforce observability from day one, and maintain vendor-neutral portability across model providers and infrastructure stacks.

You will learn how to:

Design context pipelines that prevent drift and control token costs
Implement structured world-state management for deterministic agents
Add production-grade observability, tracing, and performance metrics
Enforce policy, auditability, and compliance boundaries at the platform layer
Avoid vendor lock-in through abstraction and protocol-driven design
Scale multi-agent workflows without losing control of reasoning paths

Why do most AI projects fail after proof-of-concept? Because they treat AI like a feature instead of a platform. This book shows you how to engineer it properly.

If you are an AI platform engineer, enterprise architect, or senior developer responsible for reliability and governance, this is your operational manual.

Build systems that think clearly. Operate transparently. Scale responsibly.

Get your copy today and start engineering AI the way production demands.

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