Task-agnostic refers to algorithms or models that can be applied to various jobs, regardless of the specific task being performed. The same model or algorithm can be used for different problems, such as classification, regression, or generation. Task-agnostic approaches can be applied to a wider range of problems without additional modifications. They also make it easier to develop and implement machine learning systems, as they can be trained on multiple tasks and reused across different applications.

Examples of task-agnostic approaches in machine learning include transfer learning and multi-task learning. In transfer learning, a pre-trained model is fine-tuned on a new task, allowing the model to leverage its existing knowledge and reduce the amount of data required for training.