However, due to the inevitable prediction inaccuracy, this data-driven prediction model can only serve to provide good initial eluent composition guesses to try, and chemists still need to determine the eluent suitable for practical separation processes by conducting iterative trial-and-error experiments based on their own experience and the polarity-controlled separation principles in TLC. To enable automated identification of optimal eluent composition, we implemented Separation Instructor agent to replace chemists for making eluent composition decisions during the iterative TLC experiment. Here, TLC experiments were performed manually, but the automated TLC device is commercially available to achieve closed-loop optimal eluent composition identification in an autonomous manner. Upon inputting the initial TLC outcome of 12b separation at hexane : ethyl acetate = 1:1 ratio into Separation Instructor, it advised to reduce the polarity of the eluent to decrease the Rf value of 12b. Following two iterative decision-and-experiment rounds, Separation Instructor finalized the eluent composition (hexane : ethyl acetate = 3:1), under which the product's Rf value was 0.28 with 0.49 Rf value for the impurity, providing a sufficiently large difference for effective separation. Subsequently, this optimal eluent composition was used in the automated preparative column chromatography system to successfully separate the product, yielding 915 mg of the product (12b) with the isolated yield of 86% and a purity >98% (Fig. ).