Life-like animated interface agents for knowledgebased learning environments can provide timely, customized advice to support students' problem solving. Because of their strong visual presence, they hold signi cant promise for substantially increasing students' enjoyment of their learning experiences. A key problem posed by life-like agents that inhabit arti cial worlds is deictic believability. In the same manner that humans refer to objects in their environment through judicious combinations of speech, locomotion, and gesture, animated agents should be able to move through their environment, and point to and refer to objects appropriately as they provide problemsolving advice. In this paper we describe a framework for achieving deictic believability in animated agents. A deictic behavior planner exploits a world model and the evolving explanation plan as it selects and coordinates locomotive, gestural, and speech behaviors. The resulting behaviors and utterances are believable, and the references are unambiguous. This approach to spatial deixis has been implemented in a life-like animated agent, Cosmo, who inhabits a learning environment for the domain of Internet packet routing. The product of a large multidisciplinary team of computer scientists, 3D modelers, graphic artists, and animators, Cosmo provides realtime advice to students that is \deictically believable" as they escort packets through a virtual world of interconnected routers.