MCP makes it much easier for AI applications to discover and use tools, data and external capabilities. But connecting an agent to more tools also creates a new engineering problem: how do we keep the system reliable, observable and controlled as the number of tools grows?

This lightning talk looks beyond the first MCP demo and explores what changes when MCP becomes part of a real AI application architecture. We will examine tool discovery, context, permissions, failure handling and the operational challenges that appear when agents depend on multiple external capabilities.

The focus is not on building another MCP server from scratch. Instead, we will look at the engineering questions teams should answer before allowing an agent to interact with real systems: What should it be allowed to access? What happens when a tool fails? How do we know which tool was used and why? And how do we keep an expanding tool ecosystem manageable?

Attendees will leave with a practical checklist for thinking about MCP as infrastructure rather than simply another AI developer tool.