Benchmark

The loop-engineering wave is real, and so is the spend. We ran 90 real fix-until-green agent loops with LoopGain watching the failing-test count. The convergence math held perfectly at session scale — zero incoherent classifications in 90 loops — and it stops cleanly when sessions have budget (30/30). But the stop rule has an honest bug: on a hard, budget-tight cell it false-stopped 13 of 30 times, 9 of them one session before the fix would have landed. We traced it to a hardcoded rule in our own core, measured the savings honestly (big vs a naive loop, modest vs a smart one), and caught ourselves nearly publishing a third ‘finding’ that was really a test artifact. Here’s all of it.

Our benchmark headline is a 92.8% cost reduction. The useful part is everything that didn’t fit the headline: the state we built to catch oscillation mostly catches stalls, the savings are loaded toward easy cases, and on normal workloads we mostly preserve quality rather than improve it. Five honest surprises.