Something an agent made drifts into production and runs. No error, no warning, dashboard green. Three weeks later you discover it's been quietly miscomputing a number the whole time — not because anything cried out, but because someone happened to look closely. Three weeks of wrong data, just because the failure was silent.
This is the scariest thing about the field: a loud error you know about instantly and fix. A silent one kills you — it makes no sound, so it buys time, and time turns a small bug into a big cleanup.
01Three ways to catch it while it's early
Put your check right where the agent's work meets the real world — real output, real numbers. Don't trust the internal state; look at what it emits.
"No error" isn't "correct." A job that finishes with nothing to look at — no log, no result to compare — is suspicious, not reassuring.
An edge case that once bit you — keep it, run it every time. If it ever goes quiet, you know the marker slipped, before the real world finds out for you.
All three share one spirit: don't wait for the real world to report the error to you. The real world reports very late and very expensively. You actively place spots where a failure must surface early, in your hands, while it's still cheap to fix.
02"Done" is when you start looking, not stop
The final trap, closing the cluster: we tend to treat "done" as the finish line — it's done, so we turn away. But for something that fails quietly, "done" should be when you start watching: place the measure, check the marker, follow the boundary long enough to be sure it holds in the wild, not just in the demo.
Not forever — long enough for the demo–field gap to show whatever it's going to show. A silent failure only wins when no one is looking. Look at the right spot, at the right time, and you take away its biggest weapon.