A novel idea of ubiquitous media agents is presented. Media agents are intelligent systems that are able to automatically collect and build personalized semantic indices of multimedia data on behalf of the user whenever and wherever he/she accesses/uses these multimedia data. The sources of these semantic descriptions are the textual context of the same documents that contain these multimedia data. The URLs of these multimedia data are indexed using these textual features. When the user wants to use these multimedia data once again, the media agents can also help the user find relevant multimedia data and provide proper suggestions based on the semantic indices. The media agents can also learn form the user's interaction records to refine the semantic indices and to model the user intentions and preferences. In our experiments, the media agents are effective in gathering relevant semantics for media objects and learning to provide precise suggestions when the user wants to re-use relevant media objects again.