An AI agent classifying invoices can loop due to several factors. Primarily, ambiguous or conflicting invoice features often cause models to repeatedly attempt different classifications without converging, especially when encountering edge cases not adequately represented in training data. This issue is compounded by model limitations or insufficient training data diversity, making the agent struggle with novel invoice layouts or content and getting stuck in a repetitive decision cycle. Furthermore, flawed decision logic or an improperly configured feedback loop can reinforce incorrect classifications, preventing the agent from learning and breaking the cycle. In some instances, the loop might also stem from algorithmic errors or resource contention within the classification process itself, leading to a computational deadlock. All these scenarios prevent a definitive classification and result in persistent, unproductive attempts. More details: https://infok.com.ua/