Forecast Model Drift Monitor Agent

Predictive models for demand or churn lose accuracy quietly as conditions change. Each week this agent compares every model's predictions with actual outcomes and checks whether the input data has shifted, such as a new customer mix. If accuracy stays above the threshold, it logs the result. When accuracy drops more than the set amount, it finds which inputs changed and suggests retraining, with its reasons. The data team does the retraining; the agent only watches, explains and compares. After retraining, it tests the new model against the current one on recent data. If the new model does not do better, it reports that and the old model stays in place. The CDO approves any switch. Everything goes into the model log. Edge case: a holiday week distorts accuracy, so the agent excludes that week from the comparison and marks it in the log.