Researcher quantifies agent harness effects: heavy tool overlap across frameworks, system prompts matter

On October 7, researcher @yb2698 released an HF Space for a comparative study of agent harnesses (model evaluation/execution frameworks), along with a series of posts systematically quantifying how frameworks and model scale affect performance. The author noted that harnesses influencing model performance is known in the community and recent work has mentioned it, but it deserves systematic verification.

Confirmed

• Tool sets across frameworks overlap by more than 65%; the bare function definitions across harnesses contain many identical function signatures with only minor divergences, meaning some tools can be reused across harnesses as-is.
• The same functionality is often defined redundantly—e.g., some harnesses expose both exec and bash terminal execution tools; other harnesses are minimal, exposing very few tools, yet still perform strongly on real tasks.
• Experimental setup: on 25% of a recent tier subset of the ALE benchmark (11 tasks total), run Qwen models of different sizes with two harnesses (Qwen Code and Pi), reporting average reward and average token consumption.
• The best overall result came from the 27B model + Pi harness, but its token consumption was nearly 1.5x that of Qwen Code.
• Changing the system prompt—the simplest variable—also significantly affected performance: v1 appended instructions after Pi's default prompt, while v2 replaced it entirely with one minimal sentence.

Why it matters

This work provides testable, quantitative evidence for the widely held view that "harnesses affect model evaluation results," with all materials publicly available via the HF Space for community verification; it offers direct reference value for evaluation comparability and agent tool design (redundant definitions vs. minimal tool sets).

2026-10-07 ~ 2026-10-07 · 6 related posts

• Some agent harnesses duplicate tools while minimal ones stay powerful — yb2698 · 2026-10-07
• Identical function signatures found across agent harnesses — yb2698 · 2026-10-07
• [source] Agent harness analysis finds 65%+ tool overlap across frameworks — yb2698 · 2026-10-07
• New experiment series quantifies how harnesses shape model performance — yb2698 · 2026-10-07
• [source] Benchmarking Qwen sizes across Qwen Code and Pi harnesses on 11 ALE tasks — yb2698 · 2026-10-07
• [source] Best-scoring harness combo burns nearly 1.5x more tokens than Qwen Code — yb2698 · 2026-10-07