One of the core 设计 advantages I work with in AINNA NeuralOps AI 智能体 is that it accepts two communication modes: technical specifications or plain human language.
On the 系统 side, I can hand the agent precise technical instructions with exact constraints; in the field, an operator can describe the same outcome in everyday language. The agent resolves intent, maps it to the right execution path, and runs it.
That changes how people interact with AI at the integration layer.
You no longer need to master prompt engineering to control AI. You need to know the outcome you want and the constraints the 系统 must respect.
AI should adapt to human operators, not force them to learn a prompt dialect before they can use the 系统.
That's the direction we're building into AINNA NeuralOps.
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收藏了，主要是为了technical specifications or plain human。
AI should adapt to human这个说法我要拿回去跟同事讨论。
我会把that's the direction we're building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maps it to the right。
关于that's the direction we're building的实际落地部分最吸引我。