Benchmarks are either saturated or brutal right now, and neither number tells you what actually kills a deployment

I have been looking at benchmark scores this week and there is a huge split. On one end, models are basically tied at the top, differences of half a point that don't mean anything. On the other end, the harder new benchmarks are brutal, top models scoring around a third of what human experts hit on the same tasks. Neither number is what actually predicts whether an agent survives production though. What keeps coming up in the enterprise post-mortems is a completely different failure mode, not "the model got the task wrong," but "the model didn't know the task wasn't worth doing" or picked confidently between two reasonable-looking options and picked the wrong one for the actual business context. No benchmark I've seen scores judgment, they all score task completion. It just feels like most of the industry's still arguing about which model wins the leaderboard while the actual gap that kills deployments is somewhere the leaderboard doesn't look at all. I would like to know if anyone's found a way to actually evaluate judgment before shipping, or if that's still purely a "find out in production" problem.