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JIT-Agent: Scaling Harness Intelligence via Just-in-Time Harness Evolution

arXiv cs.CL LLM Agents Guibin Zhang, Leo Lu, Fangzhou Xie, Kang Zhu, Junhao Wang, Zhifei Xie, Zhaochen Yu, Zihang Liu, Zhongxiang Sun, Qiankun Li, Yue Liao, Heng Chang, Xiaobin Hu, Qibing Ren, Wangchunshu Zhou, Shuicheng Yan 2026-08-26

TL;DR - JIT-Agent is a model that generates and repairs task-specific agent harnesses at runtime, adapting memory, planning, action protocols, and tool orchestration for off-the-shelf LLMs. The results suggest harness optimization can improve agent performance independently of—and sometimes more than—switching to a stronger foundation model.

Formalizes agent harnesses as machine-generatable artifacts built from a fixed four-module protocol.
Learns to customize harnesses per task, repair them for reliable execution, and evolve them using archived performance signals.
With generated harnesses, DeepSeek-V4-Flash reportedly exceeds GPT-5.6 by 9.1 points on DeepSearchQA and 4.3 on OdysseyBench; GLM-5.2 gains up to 20.2 points.
Generated harnesses are competitive with mature runtimes such as OpenCode and Claude Code and consistently improve several model families.

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