MCP is everywhere, but don’t panic. Here’s why your existing APIs still matter.

MCP's rise doesn't invalidate our API investments—it actually depends on them. As someone building data platforms, I see MCP as a protocol layer that standardizes how AI agents interact with our systems, not a replacement for the data access patterns we've already built. The real shift is architectural: we're moving from humans querying APIs to agents doing it autonomously, which means our existing REST endpoints and data contracts become more critical, not less. The operational implication is that data teams need to think about API observability and governance differently. We can't just log human queries anymore; we need audit trails and rate limiting designed for agent-scale traffic. This connects to the broader platform engineering movement where data infrastructure becomes consumed by AI applications. My recommendation: audit your current API layer for agent-readiness. Ensure proper authentication, clear error handling, and schema documentation that LLMs can parse. Your existing Kafka topics, dbt models, and database schemas don't need to change—but the interfaces serving them should be designed with autonomous consumers in mind.