As AI adoption shifts from experimentation to real delivery, the way intelligent applications connect to enterprise systems needs to mature. Traditional API patterns often introduce unnecessary friction, tight coupling, and governance gaps, especially when LLMs are involved. This session provides a practical and grounded look at implementing the Model Context Protocol (MCP) on Azure in a way that is secure, governed, and ready for scale.
We will explore how MCP reshapes integration by wrapping existing capabilities in schema-rich context. This makes tools easier to discover, more consistent to use, and far more maintainable over time. You will see clear architecture patterns that translate directly into practice: hosting MCP servers on Azure services such as Container Apps or Functions, applying policy and lifecycle control through API Management, enforcing identity and authorization with Microsoft Entra ID and Managed Identity, protecting secrets with Key Vault, and building confidence through full observability using Azure Monitor and Log Analytics.
We will compare MCP with traditional API approaches, highlight key design differences, and walk through live demos. These include setting up an MCP server, registering tools, and connecting clients in a way that preserves reliability, auditability, and governance at every step.
Who should attend
Solution Architects, AI Developers, Platform Engineers, and technical decision makers focused on simplifying integration and accelerating AI delivery.

What you will learn
- Why traditional APIs struggle in AI and agent-centric workflows, and how MCP addresses those limitations.
- How to implement enterprise-grade MCP on Azure with proper governance, security, observability, and lifecycle management.
- How MCP enables smarter and more adaptable AI applications without introducing operational risk.

If you want to move beyond proofs of concept and build AI systems that scale with confidence, this session is designed for you.