HarnessLens evolves agent harnesses under a verification budget

Automated harness evolution usually scores every candidate modification on a fixed task set, letting aggregate scores hide specific regressions. HarnessLens instead jointly explores the task space and user-configurable components, derives candidate modifications from execution trajectories, and verifies each candidate only on behavior-relevant tasks selected through an attributable-evidence gate. Across three agent harnesses and four benchmarks it improves average held-out performance by 7.6-13.6% while consuming substantially less evaluation budget than propose-and-verify baselines. Code is released on GitHub.

This is the second independent result in a week (after AutoSaddler, 8/26) showing the harness is a measurable optimization target, and the new angle is cost: behavior-aware verification makes the evolution loop affordable when each evaluation is expensive. The attribution gate is the piece worth copying into your own eval-driven prompt and tool-tuning loops.