To demonstrate LLM-based agents copiloted reaction optimization workflow (Fig. ), we conducted the condition optimization for the selective oxidation of diol (12a) to the corresponding mono-oxidized aldehyde product (12b). The reaction design space included two continuous variables The self-driven optimization system iteratively conducted reactions and proposed candidate experiments based on existing reaction results, thus gradually improving the reaction yield of 12b (Fig. ). Multiple high-yield reaction conditions were identified within the design space (Table ). To automatically stop the reaction optimization task when the expectation of further yield improvement was diminished, we compared the statistical stopping criterion and stopping decision given by the LLM-based agent Result Interpreter. The probability of improvement (PI) metric, a typical statistical stopping criterion , was first examined by stopping the optimization when the cumulative number of proposed reaction conditions with PI values below 0.01 reached two. This PI stopping criterion was met after completing 36 experiments (Fig. ), based on which the optimal conditions should be confidently identified. In comparison, Result Interpreter was used to determine the appropriate stopping point for the optimization task using the concept of balancing exploration and exploitation for black-box function optimization (Supplementary Information Section 5.3). During the exploitation of CuBr2-DBU combination (after 12 experiments), Result Interpreter indicated that the yield was sufficiently high to consider stopping optimization, however, it still recommended further exploration in copper catalysts based on exploration considerations. Then, the BO continued to explore two more catalysts (i.e., Cu(BF4)2 and Cu(OTf)2). After several small condition adjustments proposed by the BO near the high-yield conditions, the reaction yield did not increase significantly, and a yield decrease was observed in the 22 nd experiment. Result Interpreter once again suggested considering the cessation of the optimization. After the 26 th experiment, Result Interpreter assessed the reaction yield as sufficiently high and the exploration of the reaction space as comprehensively executed, explicitly recommending the termination of further optimization (Fig. ). This comparison showed that the The interaction between human chemists with Experiment Designer for reaction scale-up and Separation Instructor for finding the optimal eluent composition (see detailed interaction dialogues in Supplementary Information Section 5). c) Radar chart of the reaction conditions for selecting the most potent reaction condition for scale up. d) H NMR spectrum of the purified target product (12b) in DMSO-d6.