Agent Approval Should Hinge on Action Consequences, Not Model Confidence

A DEV Community article argues that approval systems for AI agents often ask whether an action is safe rather than what happens if it is wrong and whether it can be undone. It uses a hypothetical procurement agent to illustrate how routing every payment to a human can produce rubber-stamp approvals and late reviews after key decisions are already made. The author proposes setting review thresholds by consequence and reversibility, reserving human approval for actions that send data externally, change permissions or financial details, create external commitments, or rely on ambiguous instructions. Approval requests should specify the proposed action, its scope, the authority used, and the resulting effects. The piece concludes that confidence scores can help prioritize review but should not determine whether it is required.