TLDR
基于大语言模型（LLM）的 Agent 在平均准确率上表现良好，在生产中却不可靠，这一差异已被观察到却基本未得到解决。在 AppWorld benchmark 上，使用 GPT-4.1 的 ReAct Agent 面对同一任务运行五次时，仅 53% 全部成功，而单次通过率平均达 77%。我们将这 24 个百分点的缺口称为一致性差距，并认为弥合它是可信 AI Agent 部署的前提。我们提出一种自进化 Agent 框架，通过识别不稳定、低一致性的……Large language model (LLM)-powered agents can be accurate on average yet unreliable in production, a discrepancy that has been observed but remains largely unaddressed. When given the same task five times, a ReAct agent on the AppWorld benchmark using GPT-4.1 succeeds in all five runs only 53% of the time, even though its per-run pass rate averages 77%. We call this 24-point shortfall the consistency gap, and we argue that addressing it is a precondition for trustworthy AI agent deployment. We present a self-evolving agent framework that reduces this gap by identifying unstable, low-consistenc