An AI hypothesis should be easy to overturn when infrastructure evidence disagrees with it. That is a feature, not an embarrassment.
The trust problem is not fluency.
Network engineering already contains unusually strong sources of truth: interface state, routing adjacencies, RIB and FIB entries, policy, packet paths, timestamps, configuration deltas, and controlled tests. An AI system that reasons about networks should use those sources to constrain its conclusions rather than treating model confidence as evidence.
A useful failure mode
Suppose a system observes that a BGP adjacency disappeared after a change and hypothesizes a routing-policy defect. That is plausible. It is not yet a fact. If the carrier interface went down at the same timestamp and no policy delta exists, the network has supplied evidence that weakens or rejects the initial theory.
The model may generate the hypothesis. The evidence must be allowed to reject it.
Humans need the same discipline.
The human statement “these two carriers are redundant” is also a claim. If both paths share the same building entrance or metro facility, architecture evidence should be able to challenge the human assertion without turning the interaction into a contest between person and machine.
The objective
The strongest system is not the one that prevents disagreement. It is the one that converts disagreement into a precise evidence request, records what changed the conclusion, and leaves consequential authority with the accountable human.