AI researchers are striving to build complex multi-agent worlds with intended applications ranging from the RoboCup robotic soccer tournaments, to interactive virtual theatre, to large-scale real-world battlefield simulations. Agent tracking --- monitoring other agent's actions and inferring their higher-level goals and intentions --- is a central requirement in such worlds. While previous work has mostly focused on tracking individual agents, this paper goes beyond by focusing on agent teams. Team tracking poses the challenge of tracking a team's joint goals and plans. Dynamic, real-time environments add to the challenge, as ambiguities have to be resolved in real-time.