Many current and potential AI applications are intended to operate in dynamic environments, including those with multiple agents. As a result, standard AI plan-generation technology must be augmented with mechanisms for managing changing information, for focusing attention when multiple events occur, and for coordinating with other planning processes. The DIPART testbed (Distributed, Interactive Planner’s Assistant for Real-tlme Transportation pIanning) was developed to serve as an experimental platform for analyzing a variety of such mechanisms. In this paper, we present an overview both of the DIPART system and of some of the methods for planning in dynamic environments that we have been investigating using DIPART. Many of these methods derive from theoretical work in real-time AI and in related fields, such as real-tlme operating systems.