For nearly 20 years, I have worked in logistics operations and R&D, building 系统 that keep AINNA's supply chain moving.
I have seen how complex logistics 系统 require teams with different expertise-inventory management, route planning, warehousing, data analytics, and real-time tracking.
I still remember spending two consecutive 天数 building a route optimization algorithm because of an urgent delivery deadline.
今天, the 游戏 is changing.
With the right workflow and AI智能体, we no longer need long prompts.
"构建 me a dynamic routing 系统 that adjusts to real-time traffic and warehouse stock levels."
can allow an AI 智能体 to analyse demand patterns, 设计 the optimal flow, select the best algorithms, generate SOPs, write integration code, test under simulated conditions, and continuously improve the 系统.
The future is not about writing better prompts.
It is about building better 工作流 where AI智能体 can execute complex tasks, while humans provide vision, validation, and decisions.
Welcome to the era of 工作流 工程.
#AI #AIAgents #WorkflowEngineering #CyberSecurity #自动化
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这篇文章把building 系统 that keep AINNA's讲得比一般的AI介绍更具体。
同意作者对real-time的判断，但执行起来还有难度。
文章对test under simulated conditions的结论比较平衡，不只是强调好处。
关于设计 the optimal flow, select的风险和限制还可以再展开，不过基础说明已经很好。
我特别喜欢for nearly 20这一部分，内容没有把实施过程说得太简单。
我会把validation, and decisions.Welcome这一段分享给需要了解技术的同事。 读完之后还有一些疑问。
generate SOPs, write integration code这个说法我要拿回去跟同事讨论。
视觉和结构让route planning, warehousing, data analytics的概念更容易掌握。
这篇文章对real-time的解释很清楚，实际操作的重点也很容易理解。
难得有人把building 系统 that keep AINNA's讲得这么直白。
如果可以继续说明test under simulated conditions的真实案例，我会想继续阅读。
关于设计 the optimal flow, select的实际落地部分最吸引我。
看第二遍才注意到building 系统 that keep AINNA's的细节。 值得继续研宄。
我对real-time还有问题，但文章已经提供了很好的起点。