Integrated information, denoted as \(\varPhi \) , quantifies the intrinsic information within causal systems. Despite its profound theoretical implications, applications of \(\varPhi \) have mostly taken place in simulations of arbitrary systems, particularly in terms of biological realism. This study applies \(\varPhi \) calculations to biologically inspired robotic agents that adapt to environmental conditions, thus providing a novel context for observing changes in information integration. The agents’ neural network is evolved to demonstrate behavior similar to Braitenberg’s Vehicles. The neuro-mechanical design of these evolved agents are then suitable for \(\varPhi \) analysis. Interestingly, early generations had higher \(\varPhi \) values. In later generations the diversity of connection weights and the \(\varPhi \) values decreased, leading to simpler and more reactive neural activations.