AI & ML
Agents That Know When to Stop
Autonomy is easy to grant and hard to bound. Calibrated stopping may matter more than calibrated answers.
by Dr. Priya Nair, Machine Learning · April 16, 2026 · 8 min read
An agent that never quits is not ambitious; it is broken. The hardest part of building autonomous systems is not capability but knowing the boundary of competence.
Calibration — a model's sense of its own uncertainty — turns out to be the load-bearing skill. An agent that knows it doesn't know can ask, defer, or stop.
We are learning to train this directly: rewarding honest abstention, penalizing confident error more than admitted ignorance.
The agents worth trusting will be the ones that hand control back at exactly the right moment — neither too soon to be useful nor too late to be safe.