The most dangerous failure in AI is the one you don’t measure

Here’s something uncomfortable I’ve learned building AI agent systems:

AI rarely fails at the step we’re watching.

It fails somewhere quieter —
a retry that hides a timeout,
a queue that grows by every hour,
a memory leak that only matters at scale,
a slow drift that looks like “variation” until it’s too late.

Most teams measure accuracy.
Some measure latency.

Almost no one measures degradation.

But that’s where production breaks:
not in a single crash,
but in the compounding effects we never instrumented.

Curious to hear from PH,
What’s the smallest signal that ended up predicting your biggest AI failure?