Technology Innovation Institute argued that enterprises need proof—rather than promises—before deploying AI agents that retrieve sensitive data, execute actions through tools and APIs, and update live systems. The report stresses that agent failures are harder to contain than chatbots because the damage can occur at machine speed when agents move money, alter records, or push changes into production. TII says trust must shift from model-output evaluation toward execution-time accountability, including what an agent did, which code and model ran, where it executed, what data it accessed, and whether it stayed within enforceable limits. It points to governance building blocks such as oversight committees and control planes, but highlights a remaining gap in independent verification at the moment of action. For universities and research teams exploring AI-enabled operational workflows, the report signals a compliance and risk-management checklist for agentic systems that may touch student data, records, and campus services.