Architecting the Data Layer for AI Agents: From Transactional Systems to MCP and Semantic Models

Most conversations about production AI agents focus on the agent itself — the prompts, the orchestration, the framework. But the moment you put an agent in front of real enterprise data, a different problem dominates: the data layer wasn’t designed for this consumer. Data lakes were built for analysts and dashboards. Transactional systems were built for applications. Neither was built for a non-deterministic, token-hungry, latency-sensitive reasoning loop that may issue thousands of unpredictable queries per minute.