AI Engineer · October 11, 2026

Agent benchmarks: report repeatability, incomplete runs, and total cost

video Model Evaluation Agent Security AI Engineering

Agent benchmarks: report repeatability, incomplete runs, and total cost video thumbnail

Why it matters

Wolfram Ravenwolf’s WolfBench workflow retains the configuration and trajectories behind an agent score. It compares average, best, and worst runs with the tasks solved at least once and those solved in every observed run. The public collection and dashboard code distinguish incomplete or invalid runs from comparable results. This makes variance and cost visible, while avoiding the claim that success in a few repeats guarantees future reliability.

My takeaway: Pin the benchmark, harness, sandbox resources, agent, model, timeout, and provider settings. Repeat runs, report inclusion rules, and preserve failed trajectories. Compare cost per completed task and consistently solved tasks alongside the headline average.

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