The goal of our work is to develop architectures for general intelligent agents that can use large bodies of knowledge to achieve a variety of goals in realistic environments. Our efforts to date have been realized in the Soar architecture. In this paper we provide an overview of plan execution in Soar. Soar is distinguished by its use of learning to compile planning activity automati-cally into rules, which in turn control the selection of operators during interactions with the world. Soar’s operators can be simple, primitive actions, or they can be hierarchically decomposed into complex activities. Thus, Soar provides for fast, but flexible plan execution.