xMagic outlines human-in-the-loop handoff metrics

The author argues that human-in-the-loop workflows in enterprise AI should be judged by how much completed work remains when a human steps in, rather than purely by automation rate. Well-designed handoffs provide necessary context such as gathered data and explicit recommendations, while autonomy should be earned incrementally on evidence. Real-world cases from Air Canada, Klarna, and medical screening demonstrate that ineffective human oversight fails to catch errors when review steps are poorly designed or treated as a rubber stamp. Open article →