Governance, Policy & Oversight

Ownership, risk tiers, escalation, kill switches, accountability.

• Institutional AI governance structures: who owns agent design, deployment, monitoring, and retirement.
• Agent-specific policies: authorization, scope of action, escalation, human-in-the-loop requirements, auditability, and kill-switch mechanisms.
• Risk classification of agents, from low-stakes information retrieval to high-stakes advising, financial, or academic decisions.
• Integration with broader AI policy, data governance, academic integrity, and equity policies.
• Multi-institutional or system-level coordination, especially for multi-campus systems.
• Accountability models when agents act, recommend, or initiate workflows.