An AI agent can loop when summarizing support tickets primarily due to ambiguous or ill-defined instructions in its prompt, lacking clear termination criteria for the summary task. This prompts the agent to continuously refine or re-evaluate its output without achieving a definitive "stop" state, as it perceives no end to the summarization process. A critical contributing factor is self-referential processing, where the AI might inadvertently begin to summarize its own previous summary attempts, becoming trapped in an infinite feedback loop. Additionally, when faced with exceptionally complex, contradictory, or excessively verbose ticket data, the agent may struggle to converge on a stable, concise summary, leading to repeated attempts to process and re-evaluate the information. A poorly designed reward function in reinforcement learning scenarios or insufficient fine-tuning for summarization tasks can also exacerbate these tendencies, resulting in persistent looping behavior. More details: https://clckto.ru/rd?kid=18075249&ql=0&kw=-1&to=https://infoguide.com.ua