To investigate why the AI might be behaving unpredictably, I’d start by stepping away from the aggregated metrics and digging into the model’s behaviour slice by slice. The first thing I’d do is map out where inconsistencies are most likely to hide different geographies, user types, traffic spikes, model versions, and confidence bands. Instead of looking at overall accuracy, I’d break the logs into these segments and compare how the model performs across each one. This usually reveals quiet trouble spots that dashboards smooth over. I’d also look closely at rare error patterns by isolating high-confidence misclassifications, sudden drops that correlate with specific data distributions, and any cases where the model’s predictions flip unexpectedly for similar inputs.