real-time monitoring techniques are rarely incorporated in current SDLs employed in chemical procedures. Going further, workup procedures inherently rely on macroscopic visual observations such as color changes, liquid levels, crystal formation, etc. These visual cues play a pivotal role in contextualizing experimental outcomes, aiding decisionmaking (e.g., determining when to proceed with the next step), and detecting outliers or defects. Current automation approaches, however, rarely utilize visual cues for decisionmaking. These "blind" automation approaches force chemists to perform menial visual tasks that could be easily automated, wasting valuable experimentation time and effort. Therefore, it is imperative to incorporate visual observations into automating workup processes to enhance efficiency and precision within these procedures, ultimately optimizing the overall workflow in SDLs.