Data observability has traditionally been built around human workflows. When data breaks, engineers are alerted, open dashboards, inspect lineage graphs, and manually trace the issue across pipelines. The system is designed for human investigation and interpretation. That model is now being challenged by the rise of AI agents in data operations. As organizations begin embedding AI into analytics, engineering, and decision-making workflows, observability is no longer just about explaining what happened - it must also enable systems to understand and act on it.