In summary, we have shown that the presented APE framework provides a general and straightforward approach to systematically assemble elementary process lists beyond human intuition and bias. The focus on ever new local atomic environments minimizes the redundancy in the underlying one-sided TS searches and renders APE applicable to larger system sizes and complexities that also span non-crystalline off-lattice configurations. At present we simply employ a fixed, large number of TS searches per local atomic environment. Adapting this number on-the-fly to a decreasing success in finding novel TSs or including some form of active learning, e.g. penalizing TS revisits, might create a further gain in efficiency. Nevertheless, the total number of PES evaluations required for moderately complex systems like the present Pd(100) showcase will likely strain computational budgets if directly based on first-principles electronic structure calculations like DFT. To this end, we view the emergence of machine-learned interatomic potentials (MLIPs) as a game-changing development. Combining MLIPs as fast surrogate for the DFT-PES with the efficiency of APE (and its negligible computational overhead) will make predictive-quality first-principles kMC simulations for reactive systems feasible. We have followed this path in work on the initial oxidation of Pd steps, with APE readily identifying close to 3000 unique elementary processes . Among these are numerous low-barrier collective processes that enable an O-induced step restructuring that is facile enough to introduce fluxionality aspects to the operando evolution of Pd surfaces in oxidation catalysis. This -as well as already the much simpler, but hitherto nevertheless unrecognized collective sliding motions of atomic rows in the presented Pd(100) island diffusion example -gives only but a taste for the surprising non-intuitive elementary processes that have likely been missed in the prevalent human created look-up tables so far.