How AI Acquires Professional Capabilities Beyond the Model
A foundation model can know a great deal about programming, finance, manufacturing, medicine, and law. It still cannot know the private operating knowledge of a particular organization: an internal approval rule, a proprietary engineering method, an undocumented exception, or the reason one production procedure is trusted while another is prohibited.
This gap is often described as a knowledge-access problem. Give the model the right documents, retrieve the right passages, and it will know what to do.
Retrieval is necessary, but it is not sufficient.
The real enterprise question is not only:
Can the AI find the knowledge?
It is:
Can the AI turn that knowledge into a correct, verifiable, and constrained capability?
Our work with TeaQL suggests a three-layer answer: Skill, Build Harness, and Runtime Harness. A Skill keeps the agent's direction and method sound. A Build Harness makes the constructed artifact mechanically verifiable. A Runtime Harness keeps actual execution inside engineering, security, permission, resource, and audit boundaries.
Together, these layers provide a practical way to give AI capabilities that do not exist inside the model itself.