An AI agent attempting to classify product requirements often loops due to fundamental issues like inherent ambiguity within the requirements themselves or poorly defined, overlapping classification categories. This problem is compounded by conflicting classification rules or inconsistent labels present in the agent's training data, leading to indecision. Furthermore, the absence of a designated "uncertain" or "cannot classify" category can trap the agent in an endless cycle as it tries to force a fit. Another significant factor can be insufficient confidence thresholds or scenarios where multiple categories yield near-identical probabilities, preventing a definitive choice. For reinforcement learning agents, this looping can manifest as a stuck feedback loop where exploratory actions fail to resolve the classification dilemma. Ultimately, the complexity and often informal nature of product requirement documents (PRD) make robust, unambiguous classification a considerable challenge, easily leading to such iterative failures. More details: https://nn.domoway.ru/go.php?url=https://infoguide.com.ua/