AI governance manages models and their outputs, while AI agent governance manages what autonomous agents do across enterprise systems. Traditional AI governance covers model risk, bias, and content safety. Agent governance adds authentication, authorization, real-time policy enforcement, human approval, and action-level audit, because an agent can read, write, and transact rather than only generate text. The distinction matters as agents spread: Gartner projects 40% of enterprise applications will embed task-specific AI agents by the end of 2026, up from less than 5% in 2025.