In many domains, intelligent agents must coordinate their activities in order for them to be successful both individually and collectively. Over the last ten years, research in distributed artificial intelligence has emphasized building knowledge-lean systems, where coordination emerges either from simple rules of behavior or from a deep understanding of general coordination strategies. In this paper, we contend that there is an alternative for domains in which the types and methods of coordination are well structured (even though the environment may be very unstructured and dynamic). The alternative is to build real-time, knowledge-based agents that have a broad—but shallow—understanding of how to coordinate. We demonstrate the viability of this approach by example. Specifically, we have built agents that model the coordination performed by Navy and Air Force pilots and controllers in air-to-air and air-to-ground missions within a distributed interactive simulation environment. The major contribution of the paper is an examination of the requirements and approaches for supporting knowledge-based coordination, in terms of the structure of the domain, the agents' knowledge of the domain, and the underlying AI architecture.