Real-word intelligent agents must be able to detect sudden and unexpected changes to their task environment and effectively respond to those changes in order to function properly in the long term. We thus isolate a set of perturbations that agents ought to address and demonstrate how task-agnostic perturbation detection and mitigation mechanisms can be integrated into a cognitive robotic architecture. We present results from experimental evaluations of perturbation mitigation strategies in a multi-robot system that show how intelligent systems can achieve higher levels of autonomy by explicitly handling perturbations.