We give the primary end result for agnostically studying Single-Index Fashions (SIMs) with arbitrary monotone and Lipschitz activations. All prior work both held solely within the realizable setting or required the activation to be recognized. Furthermore, we solely require the marginal to have bounded second moments, whereas all prior work required stronger distributional assumptions (comparable to anticoncentration or boundedness). Our algorithm relies on current work by [GHK+23] on omniprediction utilizing predictors satisfying calibrated multiaccuracy. Our evaluation is straightforward and depends on the connection between Bregman divergences (or matching losses) and ℓp distances. We additionally present new ensures for normal algorithms like GLMtron and logistic regression within the agnostic setting.