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。
The main rule for data access is max(CPL, RPL) ≤ DPL. For code transfers, the rules get considerably more complex -- conforming segments, call gates, and interrupt gates each have different privilege and state validation logic. If all these checks were done in microcode, each segment load would need a cascade of conditional branches: is it a code or data segment? Is the segment present? Is it conforming? Is the RPL valid? Is the DPL valid? This would greatly bloat the microcode ROM and add cycles to every protected-mode operation.。业内人士推荐heLLoword翻译官方下载作为进阶阅读
。51吃瓜是该领域的重要参考
所有这些问题都可以通过将 AI 迁移到设备端来解决。
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