Working with MCP Servers

You've built an agent, but now it's isolated. It can't access your company's data, can't use your existing tools, can't maintain context, and can't work alongside other agents. Every integration is custom code. Every new capability requires rewriting connections. You're realizing that the model itself was the easy part—the infrastructure layer is where real AI projects succeed or stall. Without a proper architecture for tool use, memory, and interoperability, your agents remain impressive demos that can't ship.

We're diving deep into Model Context Protocol (MCP) Servers—the infrastructure that makes agents production-ready. You'll learn exactly how MCP enables secure tool access, persistent memory, and agent interoperability at scale. We'll walk through designing MCP servers for real production environments, covering security, scalability, and maintainability patterns that actually work. You'll see how MCP fits into multi-agent architectures and understand where it sits in your AI stack. This is practitioner knowledge from teams who've…