不是提示词，而是蓝图：AI 工作流规划如何消除提示-响应瓶颈
Not Prompts, Blueprints
在纸上先规划 AI 工作流再执行，可消除提示-响应瓶颈，让智能体在后台自主运行多步任务。作者以 Claude 为例，过去需逐步扫描输出并输入下一条指令，如今可提前预判决策分支、拍照分享工作流后离开，投资备忘录便自动格式化并送达收件箱。
In short : Planning AI workflows on paper before execution eliminates the prompt-response bottleneck. Modern models can now hold complex multi-step tasks, enabling users to sketch decision branches & walk away while agents run in the background.
I hate to micromanage & I’ve been micromanaging AI.
A few months ago, I’d use Claude for a familiar workflow : capturing notes from a meeting, drafting a follow-up email, updating the CRM, writing the investment memo. Micromanagement at 10x speed. The agent would finish a step, then wait. I’d scan the output, type the next instruction, wait again. Prompt, response, prompt, response. I was the bottleneck in my own system.
A year ago, this was necessary. The models couldn’t hold a complex task in their heads. Now they can.
But this leverage requires planning. Now I sketch the workflow before I touch the machine. I anticipate the decision branches : what if the company isn’t in the CRM? What if the website is down or the call transcript isn’t available? I flag the gaps before the agent encounters them.
This morning’s notebook page :
I took a photo & shared it with Claude & walked away. Workflows as images work beautifully.
The agents run in the background. The memo sat in my inbox, formatted, sourced, ready to send.
Not prompts. Blueprints.
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来源：Tomer Tunguz 博客（VC 分析） · tomtunguz.com