A research team built working prototypes and proposed a new way to classify privacy choices. They also audited 30 platforms. The question was how well those platforms let humans set privacy rules for agents acting on their behalf. Most platforms give humans no way to express what data an agent may share, with whom, and under what conditions. That gap is a design choice no one has made yet. Current products blur two things the research separates. One is granting an agent access to a system. The other is telling the agent what it may do with the information it finds there. Humans can often do the first. Few products support the second. An agent can read a calendar, a document store, or an inbox. The human who granted that access has no way to say which parts stay private, which can be quoted, and which can be forwarded to another agent or service. The audit of 30 platforms turns this from a theoretical gap into a measured one. Product teams building agents that read personal or organisational data should treat privacy preference setting as a first-class design problem. The research gives them a taxonomy to work from, not a critique to absorb.