Executive Summary Enterprise AI governance largely addresses systems that generate recommendations, predictions, or content. Agentic systems introduce a different governance challenge. They can interpret context, select tools, call application programming interfaces, coordinate with other agents, and alter digital or physical states. When autonomous authority reaches industrial equipment, medical devices, logistics platforms, buildings, vehicles, or edge infrastructure, an AI error may trigger operational consequences before human intervention occurs. [1] Adoption is moving faster than control design. McKinsey found that 23% of respondents were scaling an agentic AI syste…
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