Discovering unknown adverse drug reactions (ADRs) in postmarketing surveillance as early as possible is of great importance. The current approach to postmarketing surveillance primarily relies on spontaneous reporting. It is passive and suffers from gross underreporting (<10% reporting rate), latency, and inconsistent reporting. We propose a novel team-based intelligent agent system approach for actively monitoring and detecting potential ADRs of interest using electronic patient records. We designed such a system and named it ADRMonitor. To evaluate the performance of the ADRMonitor with respect to the spontaneous reporting approach, we conducted simulation experiments on identification of ADR signal pairs (i.e., potential links between drugs and apparent adverse reactions) under various conditions. The experiments involved over 275,000 simulated patients created on the basis of more than 1,000 real patients treated by the drug cisapride that was on the market for seven years until its withdrawal by the FDA in 2000 due to serious ADRs. The quantitative simulation results show that (1) the ADR detection rate of the ADRMonitor agents with even moderate decision-making skills is 5 times higher than that of spontaneous reporting; (2) as the number of patient cases increases, ADRs could be detected significantly earlier by the ADRMonitor.