MCP gateways are becoming mandatory infrastructure for any organization deploying AI agents at scale. Here is what they actually do, what they must do, and how to evaluate one honestly.
In November 2024, Anthropic released the Model Context Protocol: a wire format for connecting AI clients to tools, data sources, and APIs. Eighteen months later, MCP has crossed 78% adoption among production AI engineering teams. The public registry has passed 9,400 servers. Anthropic, OpenAI, Google, and Microsoft all support it. Practitioners have started calling it "the USB-C of AI applications."
The protocol's success created an infrastructure problem that nobody anticipated at quite this speed. Every MCP server connection expands an organization's attack surface. Every AI agent operating with tool access can read private data, write to production systems, and execute commands under the permissions of whoever authorized it. Without a governance layer, these agents are black boxes: no audit trail, no access control, no identity attribution, no way to answer "what did this agent do?" to an auditor.
The answer the market has converged on is an MCP gateway: a control plane that sits between AI agents and the tools they call. But the term covers a lot of ground, from lightweight protocol proxies to full enterprise governance platforms. The differences are significant. Getting the choice wrong creates compliance exposure; getting it right creates the foundation for scaling AI safely.
