Present-day predictive-quality microkinetic modeling cannot address a possible dynamic impact of the operando evolution of working catalyst surfaces similar to the fluxionality discussed in nanocluster catalysis. With the novel combination of actively learned fast MLIP-surrogate energetics and automated process exploration we can overcome the first methodological reason for this limitation, namely the inability to systematically identify the elementary processes involved in such an evolution. In the application to the initial oxidation of a (110) Pd-step edge this approach readily generated process lists orders of magnitude larger than those commonly employed in state-of-the-art microkinetic modeling. Beyond just the sheer number of identified processes, many of them are also non-intuitive, involving the collective motion of multiple atoms. This level of complexity in detail (revealed here by to the diversity-driven approach inherent to APE) has barely been recognized before.