AI is increasingly taking over tasks in data engineering, such as inspecting logs, tracing pipeline failures, and proposing remediation steps, but most enterprise data platforms were designed for human engineers, not autonomous operators. These human-centered systems assume a person sits between detection and action, making decisions about pipeline restarts, data republishing, and downstream systems based on business context and dependencies. Removing that human layer requires explicit rules for decisions engineers previously made instinctively, otherwise automation becomes unpredictable. While AI agents can detect anomalies and correlate failures faster than humans, the harder question is deciding what should happen next, such as whether to retry, continue downstream processing, escalate, quarantine data, or invalidate published datasets. These operational decisions carry business consequences, and an AI may lack the organizational context to execute them safely, so autonomous systems need clearly defined decision boundaries rather than unrestricted authority. Automation can only enforce standards that already exist; if data ownership is unclear, an AI cannot determine the correct escalation path, and if lineage is incomplete, it cannot assess downstream impact.