Plan recognition, cognitive workload estimation and human assistance have been extensively studied in the AI and human factors communities, but have seldom been integrated and evaluated as complete systems. In this paper, we develop an assistant agent architecture integrating plan recognition, current and future user information needs, workload estimation and adaptive information presentation to aid an emergency response manager in making high quality decisions under time stress, while avoiding cognitive overload. We describe its main components as well as results for en experiment simulating various possible executions of the emergency response plans used in the real world, comparing reaction time of an assisted versus an unassisted human.