The research goal of this paper is to develop an advanced product search agent framework where personalized agents can meet consumer's information needs more effectively and accurately based on the Web Services, Semantic Web technologies and AI techniques. These days, one of the major bottlenecks in E-commerce is that it is not easy for consumers to find the relevant information about the products they want. Such a situation is caused mainly by inaccurate representation of consumer's search intent, and absence of appropriate product information filtering and retrieval mechanism. To resolve these problems, we developed an ontology-based personalized product search query representation methodology, an information extracting methodology specialized for semantic web-based product information, and a multi-attribute-based product scoring methodology. Furthermore, we implemented the proposed methodologies as a prototype system and validated its performance by connecting our system to the well-known Amazon.com and Buy.com.