Preparing IT for AI Agents: How MCP Shapes the Future of AI

Izzo Your AI assistant just asked to update your CRM, check inventory, and send a Slack message. Your IT team is having nightmares.

Izzo You're listening to Exploring Next, episode two-forty-six. I'm Izzo, and with me is Boone. Today we're talking MCP — Model Context Protocol — and why every IT team should care about this right now.

Boone Because we're about to hit this wall where AI agents can do incredible reasoning but can't actually touch any of our business systems in a standardized way.

Izzo Exactly. It's like having a brilliant intern who speaks twelve languages but doesn't know how to use any of our tools. So Boone, what exactly is MCP solving here?

Boone Think of it as a universal translator between AI models and enterprise systems. Instead of every AI tool building custom integrations to your CRM, database, file system — MCP creates a standard protocol.

Izzo Okay but break that down for me. What does this protocol actually look like under the hood?

Boone It's built on JSON-RPC, which gives you bidirectional communication. So your AI client — could be Claude, could be a custom agent — connects to MCP servers that wrap your business systems.

Izzo And these servers are doing what exactly?

Boone Three main things. They expose tools — like 'update customer record' or 'query inventory.' They provide resources — access to files, databases, APIs. And they handle prompts — context about how to use all this stuff.

Izzo So instead of every AI vendor writing their own Salesforce connector, they just need to speak MCP and connect to your Salesforce MCP server.

Boone Exactly. And the security model is built in. The MCP server handles authentication, permissions, rate limiting — all the stuff you need for production.

Izzo This feels like API management all over again, but for AI. Who's actually shipping this? Because I'm seeing a lot of protocols that sound great in demos but die in implementation.

Boone Anthropic open-sourced the spec and they're already using it in Claude Desktop. You can connect to GitHub, file systems, databases — it's not vaporware.

Izzo That's smart positioning by Anthropic. Get the ecosystem building MCP servers while Claude becomes the reference client.

Boone And the implementation is surprisingly clean. Each MCP server exposes a schema that describes its capabilities. The AI client can discover what tools are available and how to use them dynamically.

Izzo Wait, so the AI can basically ask 'what can you do?' and get a structured response?

Boone Right. It's like OpenAPI specs but designed specifically for AI consumption. The server says 'I have a create_ticket tool that takes title, description, and priority parameters.'

Izzo That's actually brilliant for adoption. IT teams can wrap existing systems without changing them, and AI tools get instant access. What's the catch?

Boone Orchestration gets complex fast. Once you have agents calling multiple MCP servers, managing state and error handling across systems becomes your new problem.

Izzo Yeah, I can see that. It's like microservices — great until you need to coordinate twelve different services for one business process.

Boone Exactly. And you're probably going to need some kind of workflow engine or orchestrator sitting above MCP to handle the complex multi-step operations.

Izzo Which brings us back to the original challenge — most enterprises aren't architected for this kind of AI-driven automation. This is infrastructure work.

Boone But that's also the opportunity. Companies that get their MCP servers built and their orchestration layer right are going to have a huge advantage when AI agents actually go mainstream.

Izzo I'm giving MCP itself an A-minus. Clean spec, real implementation, solves a genuine problem. The orchestration layer above it is where things get interesting.

Boone Agreed. And honestly, I'm already adding an MCP server for our internal tools to the weekend project list. The barrier to entry is really low.

Izzo Alright, for people who want to get hands-on with this — Boone, what should they actually go build?

Boone Start simple. Clone the MCP Python SDK from GitHub and build a server that wraps one internal API. Test it with Claude Desktop to see the magic happen.

Izzo And if you want to go deeper, look at the existing MCP servers — there's ones for PostgreSQL, GitHub, file systems. Study how they handle authentication and error cases. Third thing — if you're in enterprise IT, start identifying which systems would benefit most from AI agent access. CRM, ticketing, monitoring tools — that's your MCP server roadmap. MCP isn't just a protocol — it's infrastructure for the AI-first enterprise that's coming whether we're ready or not. Time to st