The World-Wide-Web is less agent-friendly than we might hope. Most information on the Web is presented in loosely structured natural language text with no agent-readable semantics. HTML annotations struc-ture the display of Web pages, but provide virtually no insight into their content. Thus, the designers of intel-ligent Web agents need to address the following ques-tions: (1) To what extent can an agent understand information published at Web sites? (2) Is the agent's understanding su cient to provide genuinely useful assistance to users? (3) Is site-speci c hand-coding necessary, or can the agent automatically extract in-formation from unfamiliar Web sites? (4) What as-pects of the Web facilitate this competence? In this paper we investigate these issues with a case study using ShopBot, a fully-implemented, domain-independent comparison-shopping agent. Given the home pages of several online stores, ShopBot au-tonomously learns how to shop at those vendors. After learning, it is able to speedily visit over a dozen soft-ware and CD vendors, extract product information, and summarize the results for the user. Preliminary studies show that ShopBot enables users to both nd superior prices and substantially reduce Web shopping time. Remarkably, ShopBot achieves this performance with-out sophisticated natural language processing, and re-quires only minimal knowledge about di erent prod-uct domains. Instead, ShopBot relies on a combination of heuristic search, pattern matching, and inductive learning techniques.