Interface agents are computer programs that employ Artificial Intelligence techniques in order to provide assistance to a user dealing with a particular computer application. The paper discusses an interface agent which has been modeled closely after the metaphor of a personal assistant. The agent learns how to assist the user by (i) observing the user’s actions and imitating them, (ii) receiving user feedback when it takes wrong actions, (iii) being trained by the user on the basis of hypothetical examples and (iv) learning from other agents that assist other users with the same task. The paper discusses how this learning agent was implemented using memory-based learning and reinforcement learning techniques. It presents actual results from two prototype agents built using these techniques: one for a meeting scheduling application and one for electronic mail. It argues that the machine learning approach to building interface agents is a.feasible one which has several advantages over other approaches: it provides a customized and adaptive solution which is less costly and ensures better user acceptability.