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AI Research
arXiv CS.AI·May 28·8 min read
Harness-Bench: Measuring Harness Effects across Models in Realistic Agent Workflows
TL;DR
AI agents rely on 'harnesses'—wrapper code that formats inputs/outputs and manages execution—but their effects on model performance remain unmeasured in realistic workflows. Harness-Bench is a benchmark that isolates and quantifies how different harness designs impact multiple AI models across practical agent tasks.
✦ Why It Matters
Engineers can now quantify harness impact on agent reliability and optimize wrapper code as a first-order performance lever.
Key Takeaways
- Measure harness impact separately from model capability to identify true performance bottlenecks.
- Test multiple harness designs against your agent tasks before scaling to production.
- Prioritize harness optimization when model upgrades show diminishing returns.
- Document harness choices as part of reproducible agent system design.
Full Summary
AI agents executing real-world tasks depend on harnesses—scaffolding code that structures prompts, parses responses, and handles errors—yet their contribution to success or failure has never been systematically measured. Harness-Bench provides a standardized evaluation framework that tests multiple language models against identical agent workflows while varying harness implementations.
The benchmark isolates harness effects by controlling for model differences, measuring how formatting choices, instruction clarity, and output parsing strategies affect task completion rates. Results show harness design can swing performance by 10-30 percentage points across models, sometimes outweighing model capability differences.
This work demonstrates that agent performance depends critically on engineering choices beyond model selection, with implications for practitioners building production systems.