The web-based comparison shopping agents (CSAs) or shopbots have emerged as important business intermediaries that provide decision support to both the shoppers and the merchants. The basic idea is to provide an easy access to both the price and non-price based competitive features to shoppers. The CSAs do not have an equivalent counterpart in the offline world and they have generated a significant amount of interest among researchers in economics, marketing, and information systems fields. There have been numerous studies on the CSAs in the contexts of price dispersion, consumer behavior, search costs, and recommender systems. The focus of this paper is to study the contemporary literature about the CSAs to analyze them in the context of decision support systems (DSS). In order to provide comprehensive decision support, a typical DSS should have four components: data, models, interfaces, and user specific customization. In this paper, this four component framework is used to synthesize the current research work in the context of DSS and to explore contemporary CSAs. The paper provides suggestions for improving the decision support aspect of the CSAs and proposes a research agenda for the CSA-based decision support systems.