Outputs are not work.
A model output can be wrong and still harmless. Agent work can change customer data, merge code, send money, trigger workflows, modify contracts, or create legal exposure.
Once agents act, teams need more than logs and evals. They need to understand what agents actually do.
The runtime question.
The key production question is: what is this agent doing, what does it mean, and what evidence supports it?
Only after that can a team decide what controls belong at runtime.
The missing discipline.
Most teams spread agent control across prompts, tool wrappers, human review, dashboards, IAM, and policy documents. That creates partial visibility but weak meaning.
Semantiv starts by reviewing behavior and assigning operational meaning before recommending controls.