What does it mean when we say an algorithm/metric is agnostic
I know of the model-agnostic term, and a close meaning would be model-independent. Basically, when you are studying a machine learning problem, the underlying structure in the data may or may not be described by one type of model or the other. The model-agnostic approach consists in using machine learning models to study the underlying structure without assuming that it can be accurately described by the model because of its nature. This avoids introducing a potential bias in the interpretation.

A model-agnostic approach pretty much requires that several different techniques are used for the same task.