Researchers introduce AutoData, an agent that searches for optimal pre-training data selection algorithms. AutoData uses a proxy model to refine its search and discovers a selection algorithm that outperforms human-designed curations. This work suggests that data engineering can be treated as an agentic machine learning problem.