This article presents a novel method for the evolution of artificial autonomous agents with small neurocontrollers. It is based on adaptive, self-organized compact genotypic encoding (SOCE) generating the phenotypic synaptic weights of the agent's neurocontroller. SOCE implements a parallel evolutionary search for neurocontroller solutions in a dynamically varying and reduced subspace of the original synaptic space. It leads to the emergence of compact successful neurocontrollers starting from large networks. The method can serve to estimate the network size needed to perform a given task, and to delineate the relative importance of the neurons composing the agent's controller network.