Why being smart isn't enough for agentic AI

#agents #ai #architecture

Agentic AI isn't just about generating answers.

It judges, acts, and actually affects your systems.

The moment that happens, "sounding smart" stops mattering.

What matters is making the same decision every time, given the same situation.

Without an explicit domain knowledge model, agents make different calls depending on the prompt and context.

The same issue might:

Get auto-fixed one day
Be routed to manual handling the next
End up as just a question another time

That might look like automation, but you can't call it production-ready agentic AI.

So instead of burying decision criteria inside prompts, I separated them into an explicit domain knowledge model.

Now the agent can explain why it made a decision—and make the same call in the same situation, consistently.

Palantir Ontology and Claude Skills both tackle this problem. Palantir Ontology impressed me by defining what AI should judge against at the system level. Claude Skills solves the same problem in a simpler form.

For this project, I didn't need enterprise-scale ontology design. I needed to quickly define and validate the decision criteria that mattered right now.

So I chose Claude Skills. Policies, actions, judgment criteria—all separated into explicit skill documents.
But defining criteria isn't the end.

An AI that takes action needs guardrails.

My agentic AI adds labels, closes issues, creates PRs, changes code.

It has real permissions in a real system.

So alongside the domain knowledge model, I built a workflow testing framework.

For each action, I defined test cases: "Given this input, this judgment must follow." Then I run actual Claude API responses to verify.

Now the agentic workflow isn't "seems to be working"—it's continuously validated against intended criteria.

Next step: a dashboard that shows the value of agentic AI in numbers, not features.

How many issues did AI handle? How much time did it save?

Without that visibility, you can't convince the team or scale further.

Moving agentic AI from "fun experiment" to "system the team can trust."