Emergence is a property often claimed to apply to complex systems on multiple levels of organization: individual behavior emerges from underlying neural activity and social patterns – from constituent behaviors of the individuals. Furthermore, the emergent level is typically characterized as possessing autonomy from the lower-level phenomena and as exerting downward causation on them. In this study, we investigate such a multi-level emergence in the context of a single simple task. We evolve agents controlled by a small neural network to travel information. We then compute measures of emergence stemming from an approach known as Integrated Information Decomposition. Results are presented for both the final behavior and the evolutionary changes that led to it.