| Summary: | Data selection, extraction and processing are frequent tasks in enterprise information systems. Their relevance emerged from the information requirements of enterprise managers to have all the possible information about their working area permanently available. They try to reach important pieces of data that will allow the extraction of useful and valuable knowledge. This knowledge can be applied in improving managers' performance inside the organization, or even in foreseeing market opportunities. Nowadays competitiveness between enterprises makes such process crucial. It is necessary, and convenient, to provide decision-makers with all possible elements that might help them in their daily activities. In fact, most of the enterprises' decision-making processes need to be global and effective, as the enterprises' success depends on it. However, the data maintained in their operational systems is not commonly arranged according to their analytical needs and management perspectives. Usually, the data is structured and treated aiming to support operational tasks and backup daily activities. Obviously, some problems arise when it is intended to orient such data to analytical purposes. Many times its structure has to be rebuilt and its quality needs to be improved. Due to today's data growth, selecting, gathering and treating data tend to be very complex and time-consuming tasks. In order to make these tasks easier, more reliable and faster, a specialized agentbased tool was developed. It was designed and conceived to support such kind of tasks and provides a set of special means of bridging to integrate operational data into specific data warehousing systems. This paper describes its main functional architecture and components, emphasizing the aspects related to its development and implementation.
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