Agent Harness Design Beats Model Tweaks

Short summary

Research on the Claw-SWE-Bench benchmark shows that optimizing agent harness design can improve coding task performance by 54 percentage points—nearly as much as upgrading the underlying model. A full adapter reached 73.4% Pass@1 versus 19.1% with minimal harness, demonstrating that engineering the execution layer matters more than tweaking prompts or parameters. Teams building coding agents should prioritize modular harness development before investing in larger models.

• •Harness design improvements (54 pp) rival model upgrades (29 pp) on SWE-Bench coding tasks
• •Full adapter reaches 73.4% Pass@1 vs 19.1% with minimal design, showing architecture dominates
• •Recommendation: prioritize cost-effective harness architecture before scaling to larger models

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