Trace Aware Benchmarking for AI Agents
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On this page 4 sections
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Key Takeaways
- Native OpenClaw scenarios show lower performance than workspace live scenarios with scores of 0.5238 and 0.6415 respectively.
- The strongest score achieved in the full profile snapshot is 0.7671.
- Reliability metrics show significant differences depending on the method, with holdout pass at k-any reliability reaching 0.6638 compared to 0.2890 for strict three-trial reliability.
- The benchmark treats failure taxonomies as audit priorities and trace localization evidence rather than final prevalence estimates.
Summary & Methodology Analysis
ClawProBench moves beyond simple success metrics by evaluating agents as a model plus runtime configuration. The methodology utilizes a full profile snapshot consisting of 68 entries and 37 clean holdout entries. By utilizing a workspace holdout as a portable contract, the framework allows for consistent evaluation across different runtime environments including OpenClaw, IronClaw, and NanoClaw. This ensures that performance is measured not just by the final outcome, but by the behavior of the agent-harness system during execution.
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Cross-Examination & FAQs
A deeper dive clarifying mechanics, constraints, and baseline evaluations.
Q1. What is the primary goal of ClawProBench?
It serves as a trace-aware evaluation framework for AI agents to assess reliability, process evidence, and safety under an auditable protocol.
Q2. Does this benchmark measure a model by itself?
No. The benchmark measures a model plus runtime configuration rather than model ability independent of a harness.
Q3. How are results reported?
Performance is reported using trace evidence, execution status, and diagnostic slices for reliability and runtime coverage.
Q4. What is the highest score observed in the study?
The strongest score in the full-profile snapshot is 0.7671.
Q5. How do native scenarios compare to workspace-live scenarios?
Native OpenClaw scenarios consistently score lower than workspace-live scenarios, with scores of 0.5238 and 0.6415 respectively.
Q6. Is the reliability metric consistent across testing methods?
No. The holdout pass at k-any reliability of 0.6638 is substantially higher than the strict three-trial reliability of 0.2890.
Q7. Does this benchmark solve the problem of public task exposure?
No. ClawProBench does not claim to eliminate the risk of public task exposure leading to overfitting.
Q8. Can the benchmark isolate specific mechanisms like routing or safety layers?
No. It holds model identities and the workspace evaluation contract fixed, but does not isolate wrapper, routing, tool-schema, safety-layer, or controller mechanisms inside that bundle.
Q9. How should the failure taxonomy be interpreted?
The failure taxonomy is generated from checker details and serves as audit priorities and trace-localization evidence rather than final prevalence estimates.