Your AI agent doesn’t need more prompts. It needs decision boundaries.

I keep seeing AI founders add longer and longer system prompts to control their agents.

But in production, the real issue is often not the prompt.

It is unclear decision boundaries.

Should the AI answer this refund question?
Should it escalate this angry customer?
Should it use old memory here?
Should it call a tool?
Should it avoid the model call because the answer is already known?

These are product decisions, not prompt instructions.

That is why I think production AI needs a runtime governance layer: something outside the model that can control policy, memory scope, escalation, traceability, and cost.

I’m testing this through NEES Core Engine with a few real workflows.

Curious: if you’re building an AI agent, where does your agent fail most — model quality, workflow logic, memory/context, cost, or escalation?