The essence of an agent is surprisingly simple: codify the know-how of an expert.

AI is already remarkably capable—it can reason, write code, analyze data, and solve many difficult problems. What it often lacks is the specific workflow and practical know-how of a particular job. It doesn’t automatically know all the details, nuances, conventions, and judgment calls that an experienced professional has accumulated.

So the job of building an agent is essentially to teach the AI how an expert actually gets a specific job done.

As I described in the previous blog about how I created the index domain expert/agent, this does not have to be a single Markdown file. It can be a layered system: Markdown knowledge, structured JSON, tools, code, workflows, tool chains, examples, precedents, and even guardrails. But underneath all that complexity is a simple idea:

Give a powerful AI the knowledge and workflow of an expert, and you turn general intelligence into domain expertise.

That’s essentially what I have done with my Index Domain Expert. I took the knowledge and workflow required to construct and maintain indexes and encoded them into an agent that can execute the job from beginning to end.

And the idea is much broader than index construction.

In principle, I could build an agent that knows how a dermatologist, podiatrist, dentist, accountant, or lawyer approaches their work. The underlying AI doesn’t necessarily need to be fundamentally different. What changes is the layer of domain knowledge, workflows, tools, rules, examples, and accumulated expertise that we give it.

The model provides the fundamental intelligence, when it’s equipped with knowledge of an expert, it is an agent!