Chromatography–mass spectrometry is a central tool for chemical and biological analysis across a wide range of applications. However, its use in automated workflows has remained limited because instrument operation, chromatogram interpretation and method development have often relied on experienced users. Here, we translate key elements of this analytical expertise into programmable operations, and apply them across workflows of increasing analytical complexity. Most importantly, we show that autonomous, machine-learning-based chromatographic method development can be accelerated substantially by moving beyond black-box optimization and incorporating analytical expertise. We further demonstrate practical use cases from synthetic chemistry and materials discovery, including the automated annotation of complex reaction mixtures, and the autonomous development of a purification method for a target compound from a crude reaction mixture. These capabilities are provided through MoSeS, an open-source Python framework that integrates sample-by-sample instrument control, analyte-resolved data processing, cross-run analyte tracking, and physicochemical retention modelling within a common software layer. Together, these results show how analytical operations that are commonly performed by experts can be formalized into reusable computational workflows. This work provides both a usable software toolbox and practical demonstrations of integrating chromatography–mass spectrometry systems into automated laboratory routines – ranging from custom data analysis pipelines to AI-guided decision making in self-driving laboratories.