Despite the rapid growth of e-commerce and the hype surrounding it, the potential of the Internet for truly transforming commerce is largely unrealized to date is because most electronic purchases are still largely nonautomated. User presence is still required in all stages of the buying process. According to the nomenclature of Maes’s group in the MIT Media Labs (Maes, 1994; Guttman & Maes, 1999), the common commerce behavior can be described with consumer buying behaviour (CBB) model, which consists of six stages, namely, need identification, product brokering, merchant brokering, negotiation, purchase and delivery, and product service and evaluation. The solution to automating electronic purchases could lie in the employment of software agents and relevant AI technologies in e-commerce. Software agent technologies can be used to automate several of the most time consuming stages of the buying process like product information gathering and comparison. Unlike “traditional” software, software agents are personalized, continuously running and semiautonomous. These qualities are conducive for optimizing the whole buying experience and revolutionizing commerce, as we know it today. Software agents could monitor quantity and usage patterns, collect information on vendors and products that may fit the needs of the owner, evaluate different offerings, make decisions on which merchants and products to pursue, negotiate the terms of transactions with these merchants and finally place orders and make automated payments (Hua & Guan, 2000). At present, there are some software agents like BargainFinder, Jango, and Firefly providing ranked lists based on the prices of merchant products. However, these shopping agents fail to resolve the challenges presented as follows.