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arXiv · 2610.32131

Pairing Matters: Performance, Failures, and Efficiency in Lightweight Coding Agents

Lu Zhao·Christopher M. Poskitt·David Lo
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Abstract

Self-hosting coding agents is often necessary for software-engineering teams that must keep proprietary code and development traces within controlled infrastructure. Lightweight, open-weight models make such deployment more feasible by reducing hardware requirements, while strong results on repository-level benchmarks demonstrate their potential for demanding coding tasks. Deploying these models as coding agents requires pairing them with an agent harness, making their joint performance a key deployment consideration. Yet existing leaderboards primarily report fixed harness-model configurations rather than controlled comparisons across alternative pairings. To address this gap, we investigate harness-model interactions through a controlled full-factorial evaluation of six open-source harnesses and three lightweight coding models across 100 SWE-bench Verified tasks, producing 1,800 graded runs under common serving, metering, and grading conditions. For the same model, resolution rates differ by up to 44 percentage points across harnesses, while harness rankings change across models. We apply an evidence-based failure taxonomy to 1,100 unsuccessful runs and find substantial differences in failure patterns across harness-model pairings. A model's dominant failure modes under one harness need not dominate under another. Efficiency is similarly dependent on the pairing: even among model-directed loop harnesses, charged tokens per resolved task vary by more than 20-fold. These findings make harness-model selection a central deployment decision for lightweight coding agents. Strong performance with one harness or model does not guarantee similar results with another. We translate these findings into practitioner guidance for pairing selection, token-efficiency assessment, and trajectory-informed harness engineering, and release the complete experimental records and analysis artifacts.

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Lu Zhao, Christopher M. Poskitt, David Lo. 2026-10-23. Pairing Matters: Performance, Failures, and Efficiency in Lightweight Coding Agents. https://arxiv.org/abs/2610.32131

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