MCP: The Missing Layer for Enterprise AI Agents

Every wave of enterprise software eventually produces a boring, essential standard — the thing nobody demos but everyone depends on. For AI agents, that standard is turning out to be MCP, the Model Context Protocol. It's not glamorous. It's plumbing. And plumbing is exactly what enterprise AI has been missing.

The integration tax

The promise of agents is that they take actions — read a ticket, query a CMDB, open a change, post an update. The reality is that every one of those actions is a bespoke integration, written differently for every model and every tool. That integration tax is where most agent projects bleed time. You don't have an intelligence problem; you have a wiring problem.

What MCP actually does

MCP standardises how a model discovers and calls tools and data sources. Instead of hand-coding a connector per model, you expose a capability once, and any MCP-aware agent can use it. It's the same shift APIs brought to web services: a common contract that decouples the thing asking from the thing doing. For platforms like ServiceNow — full of structured data and well-defined actions — that contract is a natural fit.

Why it matters for the enterprise

Standardisation is what makes governance possible. When tool access flows through one protocol, you can reason about permissions, audit calls, and swap models without rewriting your integration layer. That's the difference between a clever prototype and something you can actually run in a regulated environment.

We're early, and the ecosystem is still forming. But the direction is clear: the teams treating MCP as core infrastructure now will spend the next few years composing agents while everyone else is still writing connectors.