How to design a strong verifier for AI agent loops

LoopGain decides when an agent loop has converged. It does not decide whether your verifier is right — it trusts the error signal you give it. We measured what happens when that signal has blind spots: 4.5% of our own ‘converged’ code runs failed a fuller test than the loop ran. This is a field guide to building a verifier strong enough to trust at the stop.

June 9, 2026 · 9 min · LoopGain

We ran 2,000 paired agent-loop trials. Here's what surprised us.

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.

June 7, 2026 · 9 min · LoopGain

Get new posts by email

New writing on agent-loop control, benchmarks, and what we keep learning the hard way. At most one email per post.

Double opt-in — we'll email you to confirm. Unsubscribe any time.