НХЛ — регулярный чемпионат
从路径上看，前面提到现在智能体规模化应用集中在编程和工作流自动化方面，随着机器智能深度理解水平的提升，可以预期智能体的应用会不断拓展边界，能承担更抽象、复杂的任务，更多的自主规划和决策，来把人类的意图转化为结果。当然，突破不等于抛弃工作流。在企业高风险场景里，工作流/权限/审计会变成“护栏”，用来限制智能体的行动空间，以确保应用的安全。在相当长的时间内，人类的审批、审计在智能体工作的闭环中可能都是不可缺少的。
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Article InformationAuthor, 呂嘉鴻
为了理解母亲支离破碎的家族历史，杜耀豪踏上了旅程。（受访者供图）
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경복궁도 문 닫게 만든 BTS 광화문 공연… 26만명 몰린다，推荐阅读safew官方版本下载获取更多信息
Anthropic’s prompt suggestions are simple, but you can’t give an LLM an open-ended question like that and expect the results you want! You, the user, are likely subconsciously picky, and there are always functional requirements that the agent won’t magically apply because it cannot read minds and behaves as a literal genie. My approach to prompting is to write the potentially-very-large individual prompt in its own Markdown file (which can be tracked in git), then tag the agent with that prompt and tell it to implement that Markdown file. Once the work is completed and manually reviewed, I manually commit the work to git, with the message referencing the specific prompt file so I have good internal tracking.