Harness-Bench: Evaluating Agent Harnesses across Models and Realistic Workflows
LLM agents are increasingly deployed as executable systems that use tools, modify workspaces, and produce concrete artifacts. In such workflows, performance depends not only on the base model, but also on the harness: the system layer that manages context, tools, state, constraints, permissions, tracing, and recovery. However, existing benchmarks typically abstract away execution, compare complete agent systems, or hold the harness fixed, making execution-layer variation difficult to study. We introduce Harness-Bench, a diagnostic benchmark for evaluating configuration-level harness effects in realistic agent workflows. Harness-Bench evaluates representative harness configurations across ...
| Authors | Yilun Yao, Xinyu Tan, Chao-Hsuan Liu et al. |
|---|---|
| Primary category | cs.AI |
| Published | 27 May 2026 |
| Last updated at source | 15 October 2026 |
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