An AI agent attempting to classify incident alerts can fall into a loop primarily due to ambiguous or conflicting classification categories, where an alert fits multiple labels equally well, preventing a definitive choice. This often stems from poorly defined decision boundaries or a lack of clear stopping criteria, leading the agent to perpetually re-evaluate without converging on a single classification. Another significant factor is the presence of cyclical dependencies within its classification rules or a feedback mechanism that continuously re-triggers prior evaluations without reaching a stable state. Furthermore, insufficient or imbalanced training data for specific edge cases can cause the model to repeatedly struggle with new, unfamiliar alerts, unable to confidently assign them. Ultimately, robust AI design requires meticulous rule definition, clear confidence thresholds, and diverse training to prevent such infinite classification cycles. More details: https://www.zggkzy.com/link/link.asp?id=2123&url=https://infoguide.com.ua