MCP: The Protocol That Will Define Agentic AI Infrastructure

Every major infrastructure shift in computing has been preceded by a protocol war. HTTP standardised the web. SMTP standardised email. We're now in the early innings of the same dynamic in agentic AI, and MCP — the Model Context Protocol — is the leading candidate for the standard that sticks.

What MCP Actually Is

MCP defines a clean interface between a language model and the context it receives — files, databases, live APIs, tool results. Instead of every team building bespoke wiring between models and data sources, MCP provides a typed, versioned, auditable channel.

The analogy is useful: before REST, every API was a snowflake. After REST, there were still snowflakes, but you could reason about them. MCP is doing the same for model context.

Why It Changes System Design

Once context delivery is standardised, you can reason about it independently from model selection. You can version your context schemas. You can audit what information the model had access to when it made a decision. You can swap models without rewriting your context plumbing.

For regulated industries — finance, healthcare, legal — this auditability is not a nice-to-have. It's a prerequisite for deployment.

What to Build On Top

The teams that win in the next two years will be those who treat MCP as infrastructure and build product on top of it — not those who are still hand-wiring context pipelines from scratch. The protocol layer is becoming commodity. The value moves up the stack.