Luna could observe the shop through security camera screenshots, but still made basic mistakes, including selecting the wrong country when hiring a contractor and mismanaging staff schedules during opening weekend.
尽管AI代理在现实世界运营中展示了令人印象深刻的自主性，但它们仍然存在明显的局限性。这一事实提醒我们，当前的AI系统在处理复杂现实情境时仍不可靠，特别是在涉及细节判断和执行方面。这表明AI代理的商业化应用还需要更多的技术突破和测试。
It is not common for real software to be developed the way MirrorCode tasks are structured — against a precise, programmatically checkable specification.
这一重要提醒指出了MirrorCode评估方法与实际软件开发之间的差异。虽然该基准测试提供了有价值的AI能力证据，但如何将这种能力转化为实际开发环境中的表现仍是一个开放问题，这对AI在真实世界软件工程中的应用提出了挑战。
The system works beautifully for tracking the full universe of tasks that exists. The problem is prioritization. With multiple launches overlapping each week, figuring out which of your 30 tasks matters this morning requires mentally weighing launch dates against company strategy against what your teammates are blocked on.
令人惊讶的是：即使有完美的任务跟踪系统，优先级排序仍然是一个重大挑战，需要同时考虑截止日期、公司战略和团队阻塞情况等多重因素。这揭示了AI在复杂决策支持中的独特价值，能够处理多维度权衡。