Retrieving information from heterogeneous database systems involves a complex process and remains a challenging research area.We propose a cognitively guided approach for developing an information-retrieval agent that takes the user's information request, identifies relevant information sources,and generates a multidatabase access plan. Our work is distinctive in that the agent design is based on an empirical study of how human experts retrieve information from multiple, heterogeneous database systems. To improve on empirically observed information-retrieval capabilities, the design incorporates mathematical models and algorithmic components. These components optimize the set of information sources that need to be considered to respond to a user query and are used to develop efficient multidatabase-access plans. This agent design, which integrates cognitive and mathematical models, has been implemented using Soar, a knowledge-based architecture.