What this case covers
The author is a 2004-born AI super individual, working full-time on entrepreneurship in Hangzhou, with the core business of public account traffic acquisition and selling knowledge-paid products in private domains. The article fully breaks down the five-step process of building an Agent employee legion, covering scenario screening, knowledge base, Skill, interaction packaging, and feedback loop.
Questions it answers
- Why is conversational AI difficult to produce stable results?
- How should the first Agent scenario be screened?
- Why must the knowledge base be derived backward from tasks?
- How do Skills make AI go from occasionally correct to stable?
- What is the difference between a workbench and an AI employee?
Our observations
- Only high-frequency tasks can quickly expose problems; low-frequency scenarios are not worth building Agents for first.
- The value of a knowledge base lies in clarity, not volume; the more materials, the greater the noise.
- The feedback loop determines whether the system can evolve; manual edits and data are both indispensable.
From the author
The only purpose of AI is this: use lower cost to obtain higher output.