Modern AI development is rapidly evolving from simple prompt-response models to autonomous, tool-driven AI agents. However, connecting LLMs to internal enterprise tools, databases, and infrastructure relies heavily on brittle custom integrations, hardcoded logic, and fragmented access controls. As agentic workflows scale, maintaining security boundaries and operational visibility becomes a major platform challenge.

The Model Context Protocol (MCP) offers a breakthrough open standard for dynamic tool discovery and execution. But hosting MCP servers in enterprise environments requires robust runtime isolation, identity management, and secure API boundaries.

In this demo-driven session, we’ll look at the architectural blueprint for running production-grade AI agent infrastructure on Azure. We will explore how to host Azure MCP servers on Azure Kubernetes Service (AKS), secure inference pathways using API Management as an AI Gateway, and enforce strict Zero Trust boundaries between AI models and backend data stores.

What you will learn:
- How Model Context Protocol (MCP) standardizes agent-to-tool communication.
- Architectural patterns for deploying, scaling, and securing MCP servers on AKS.
- How to integrate MCP with Azure AI services while maintaining strict governance and data boundaries.