首先，大模型本身没那么可靠：存在无法根除的幻觉问题、知识时效性问题，任务拆解和规划经常不合理，也缺乏面向特定任务的系统性校验机制。这样一来，以其为“大脑”的智能体使用价值会大打折扣：智能体把模型从“对话”推向“行动”，错误不再只是答错问题，而是可能引发实际操作风险；而真实业务任务往往是跨系统、长链路的，一次小错误会在链路中层层放大，令长链路任务的失败率居高不下（例如单步成功率为95%时，一个 20步链路的整体成功率只有约 36%）。
Израиль нанес удар по Ирану09:28
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На Западе подчинили рой насекомых для разведки в интересах НАТО08:43
What if you create a truly unique routing profile that's wildly different from the common ones for which shortcuts were pre-calculated? The system is smart. If it detects that too many shortcuts (~50, for example) need on-the-fly recalculation and deviate significantly, it might determine that falling back to the original, comprehensive A* algorithm for the entire route would actually be faster than doing many small, heavily modified A* calculations.
Detection is via allocating a slice of zeroed memory, in our case a gigabyte, and then once per minute going through to ensure they're actually all zeroes. Magic!