Confidence is a lagging indicator: it is generated by taking action and accumulating evidence, not by waiting to feel ready. Treating confidence as a precondition creates paralysis; treating it as an output enables progress.
For agentic systems, this argues against gating actions on self-assessed readiness or certainty and in favor of evidential, action-based policies that accumulate competence through iterative steps.
An AI agent's competence should be evaluated by observed task outcomes over time, not by its internal 'confidence'—and the agent should be designed to act under uncertainty while logging evidence.
Confidence does not precede action; it is generated by taking action, building skills, and gathering evidence that you can succeed.
Waiting to feel confident is the wrong goal.