The degree to which a planner succeeds and meets response deadlines depends on the correctness and completeness of its models which describe events and actions that change the world state. It is often unrealistic to expect perfect models, so a planner must be able to detect and respond to states it had not planned to handle. In this paper, we characterize different classes of these "unhandled" states and describe planning algorithms to build tests for, and later respond to them. We have implemented these unhandled state detection and response algorithms in the Cooperative Intelligent Real-time Control Architecture (CIRCA), which combines an AI planner with a separate real-time system so that plans me built, scheduled, and then executed with real-time guarantees. Test results from flight simulation show the new algorithm enables a fully-automated aircraft to react appropriately to certain classes of unhandled states, averting failure and giving the aircraft a new chance to achieve its goals. We analyze CIRCA’s capability to accommodate challenging domain characteristics, and present flight simulation examples to illustrate how CIRCA handles each.