Traffic engineering (TE) has become a challenging mechanism for network management and resources optimization due to uncertain and difficult to predict traffic patterns. Recent works have proposed robust optimization techniques to cope with uncertain traffic, computing a stable routing configuration that is immune to demand variations within certain uncertainty set. However, using a single routing configuration for longtime periods can be highly inefficient. Even more, the presence of abnormal and malicious traffic has magnified the network operation problem, claiming for solutions which not only deal with traffic uncertainty but also allow to detect and identify faulty traffic to take the appropriate countermeasures. In this paper, we introduce the Reactive Robust Routing (RRR) for TE, an approach that combines both proactive and reactive techniques to tackle the problem. Based on expected traffic patterns, we adapt the uncertainty set and build a multi-hour yet robust routing scheme that outperforms the stable robust approach. For the case of anomalous and unexpected traffic, we propose a fast anomaly detection/isolation algorithm to detect and localize abrupt changes in traffic flows and decide routing changes. This algorithm is optimal in the sense that it minimizes the decision delay for a given mean false alarm rate and false isolation probability. We validate these proposals using real data from two different backbone networks and we show how the RRR can handle uncertain and highly dynamic traffic in an automatic fashion, simplifying network operation.