In this study, we proposed a protocol-agnostic, instrument-agnostic, and vendorneutral framework for on-the-fly detection of common errors in High-Performance Liquid Chromatography (HPLC) experiments. Leveraging a Cloud Lab's rigorous management of all experimental data provides a foundation for adapting and generalizing our end-to-end, data-driven anomaly detection framework to address rarer types of errors as they accumulate over time.