Oil refineries provide the lifeblood for global economic health, and disruptions to their operations have major worldwide impact. We are developing a large-scale intelligent refinery control system to assist human operators in controlling refineries during abnormal situations. Based primarily on reactive and procedural approaches to intelligent behavior, the Abnormal Event Guidance and Information System (AEGIS) will interact with multiple users and thousands of refinery components to diagnose and compensate for unanticipated plant disruptions. Adjusting the autonomy of AEGIS’s behavior is a key requirement for success in the dynamic, highly-unpredictable refinery environment. This paper discusses our procedural and reactive approach to the goal-setting, planning, and plan execution components of AEGIS, and the adjustable autonomy features they support.