This paper presents the architecture of a decision support system for a production environment that combines image analysis, adverse-event risk prediction, and multi-agent coordination. The study was conducted on data from a company producing precision aluminum valve bodies and components for the automotive sector. Images from station cameras, process signals, maintenance logs and planning information from MES/ERP were integrated. The proposed system uses a vision module to detect symptoms of degradation, a fusion model to estimate the risk of an adverse maintenance event within an 8-hour horizon, and an agent layer responsible for recommending maintenance activities and schedule changes. On the held-out test set, the best variant achieved F1 = 0.91 and AUROC = 0.96 and, in replay-based evaluation on historical production weeks, was associated with a 40.9% reduction in average weekly downtime relative to the reactive baseline. The results indicate that, in replay-based evaluation, the combination of visual perception and agent-based coordination is associated with higher predictive performance and better operational decision support than the compared baselines. Operational effects were estimated in a replay-based evaluation of historical production weeks rather than in a live online deployment.