Agents are the catalysts for commerce on the Web today. For example, comparison-shopping agents mediate the interactions between buyers and sellers in order to yield more efficient markets. However, today's shopping agents are price-dominated, unreflective of the nature of seller/buyer differentiation or the changing course of differentiation over time. This paper aims to tackle this dilemma and advances shopping agents into a stage where both kinds of differentiation are taken into account for enhanced understanding of the realities. We call them next-generation shopping agents. These agents can leverage the interactive power of the Web for more accurate understanding of buyer's preferences. This paper then presents an architecture of the next-generation shopping agents. This architecture is composed of a Product/Merchant Information Collector, a Buyer Behavior Extractor, a User Profile Manager and an Online Learning Personalized-Ranking Module. We have implemented a system following the core of the architecture and collected preliminary evaluation results. The results show this system is quite promising in overcoming the reality challenges of comparison shopping.