Benchmark scores and what Parsewave looks for beyond it

Whenever an AI model gets a high benchmark score, a chart gets posted online and everyone assumes it's a genius, but this score doesn’t really tell us what actually happened. An agent might generate the correct final file, but take a completely chaotic route with repeated retries or unnecessary tool calls. It might’ve even broken other things while still passing because the test only checked the final output. Although the result satisfies the benchmark’s final-state checks, these hidden costs can make the system slower, use more resources, and create more risks in real-world use. Here AI dataset companies like Parsewave come into play. Their job is to ensure the evaluation process looks beyond pass or fail i.e check execution traces, errors, retries, shortcuts, and whether the agent actually followed the task properly. Benchmark scores are still useful. They give us a quick way to compare models, but they’re more like the headline than the full story. For people working on model evaluation, what do you look at beyond the final benchmark score, failure patterns, tool use, retries, or something else?