Discovering knowledge such as causal relations among objects in large data collections is very important in many decision-making processes. In this paper, we present our development of an integrated environment acting as a software agent for discovering correlative attributes of data objects from multiple heterogeneous resources. The environment provides necessary supporting tools and processing engines for acquiring, collecting, and extracting relevant information from multiple data resources, and then forming meaningful knowledge patterns. The agent system is featured with an interactive user interface that provides useful communication channels for human supervisors to actively engage in necessary consultation and guidance in the entire knowledge discovery processes. A cross-reference technique is employed for searching and discovering coherent set of correlative patterns from the heterogeneous data resources. A Bayesian network approach is applied as a knowledge representation scheme for recording and manipulating the discovered causal relations. The system employs common data warehousing and OLAP techniques to form integrated data repository and generate database queries over large data collections from various distinct data resources.