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EvaluationExperimental

HarnessBench

by reacher-z

Compare agent harnesses by fixing the model and varying orchestration and tooling

Python
Updated Jul 17, 2026
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What It Does

Benchmarks agent harnesses by fixing the base LLM and varying the control harness to compare real-world task performance. Runs the same scoring pipeline as its sister project ClawBench to produce comparable metrics across browser and automation-focused agents. Emphasizes harness-level differences (prompting, orchestration, tool usage) rather than model differences so evaluators can isolate implementation effects, including aspects highlighted by the A2A Protocol Pattern.

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Key Benefits

As agents proliferate, differences in harness design—not just base models—drive real-world reliability and failure modes. HarnessBench makes it possible to evaluate and compare those harness-level choices reproducibly, which is essential for building a reliable agent track record and informed governance. Until now many benchmarks mixed model and harness variance; this project isolates the latter to clarify what to trust in agent deployments, a process that benefits from patterns like Mutual Verification Pattern and governance-oriented design such as Agent Registry Pattern.

When to Use

Researchers and engineers who need to evaluate how harness design (prompting, tool use, orchestration) affects agent task performance independent of model choice, guided by patterns such as Tree of Thoughts Pattern to explore reasoning structures.

Real-World Examples

  • Isolating whether performance regressions come from harness changes or model updates
  • Comparing browser-automation and tool-using harness implementations on everyday web tasks
  • Generating reproducible scores to inform agent governance and deployment decisions
Topics
agent-evaluationagent-harnessagentic-aiai-agentsbenchmarkbrowser-agentbrowser-automationcomputer-useevaluationllm+2 more
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clawbenchagent-playground
Keywords
a2a evaluationagent track recordcontinuous agent evaluation
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