A Field Study of Institutional Control in an AI-Staffed Prediction-Market Desk

This paper reports a field study of an AI-staffed prediction-market desk under human-owner governance. It argues that high-skill AI labor requires institutional control mechanisms beyond agent capability, such as work ownership, authority boundaries, evidence verification, closure processes, and organizational learning. The study introduces a framework with four pillars: state control, authority control, communication control, and learning control. Empirical evidence shows that many failures attributed to agents are actually organizational failures, and the institutional layer emerged through repeated fixes to recurring issues.