As AI agents grow in prevalence, so too do their risks to users, creating a need for robust and usable systems for agent permissions. Many recent proposals implement product-level permissions, where the same security policy is applied for all users of a given agent framework. Yet different users have varying needs, necessitating support for user-level permission policies in agentic AI systems. We survey 21 proposed user-level agent permissions systems and construct a taxonomy of approaches to specifying, deriving, and enforcing user-level permissions. We further analyze and compare against five commercial agents. We find that despite a wide diversity of proposals, sizable gaps remain in usability, transparency, and user control, particularly between academic and commercial agents.

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