时间 working with AI 智能体, one lesson has become increasingly clear to me: don’t start by telling the agent exactly how to do the job. 开始 by asking what it sees. Most of the time, we already know the outcome we want, but when we give AI overly detailed instructions too early, we also force it to follow our existing way of thinking. The AI may still deliver the same result, but the process can become unnecessarily rigid, complex, or inefficient.
I now prefer to begin with a simple request: “Brief me on what you see, what you think is not optimal, and what you recommend.” Only after that do I decide what should be executed. This changes the role of the AI agent from an instruction follower into a problem-solving partner. It may identify unnecessary steps, duplicated processes, better automation opportunities, more efficient architecture, overlooked risks, or a simpler way to achieve the same outcome.
The workflow I increasingly use is simple: observe, diagnose, recommend, challenge, and execute. 首先, let the AI understand the environment. 然后 let it identify inefficiencies, suggest improvements, challenge the current approach, and only after that proceed with execution.
One of the biggest mistakes in using AI 智能体 is assuming that better prompting always means giving more instructions. Sometimes, better prompting means giving the agent enough freedom to discover a better path. The 目标 should remain clear, but the method does not always need to come from us.
That is where AI 智能体 become genuinely useful-not just automating work, but helping redesign how the work should be done.
#AI #AIAgents #AgenticAI #自动化 #DigitalTransformation #AI生产力 #商业战略 #NeuralOps
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这篇内容让我更容易理解为什么don’t start by telling值得关注。
我会把what you think这一段分享给需要了解技术的同事。
这篇文章适合团队用来开始讨论开始 by asking。
视觉和结构让然后 let it identify inefficiencies的概念更容易掌握。
同意作者对problem-solving的判断，方向很清楚。