Verify Smarter, Evolve Further: Efficient Harness Evolution through Behavior-Aware Verification

Abstract:Agent harnesses shape how language-model agents use instructions, tools, and runtime components, but adapting these harnesses requires costly verification. Existing propose-and-verify methods typically score every candidate on a fixed task set, wasting rollouts on unrelated behaviors and allowing aggregate scores to obscure specific regressions. We introduce HarnessLens, a budget-aware framework for automated harness evolution. HarnessLens jointly explores the task space and user-configurable components, derives candidate modifications from execution trajectories, and selectively verifies each candidate on behavior-relevant tasks using an attributable-evidence gate. Across three agent harnesses and four benchmarks, HarnessLens improves average held-out performance by 7.6-13.6% while consuming substantially less evaluation budget than competing baselines. These results demonstrate that behavior-aware verification with explicit attribution enables more reliable and sample-efficient harness evolution under constrained interaction budgets. Our code is available at this https URL.

Artificial Intelligence (cs.AI)

arXiv:2608.27311 [cs.AI]

(or arXiv:2608.27311v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2608.27311 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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