Do you even benchmark? How we evaluate agents at scale (with real examples)

PostHog describes a framework for benchmarking AI agents as token spending and agent deployment scale, arguing that reliable evaluation requires task-specific evidence rather than subjective impressions. Its approach begins with increasingly sophisticated answer keys, ranging from checking agents against known bugs in a single pull request, to a golden set of real incident-causing bugs, to contextual rubrics that assess open-ended research agents based on both shared standards and task-specific criteria. The company found that benchmark integrity is threatened by agents exploiting shared memories, logs, database traces, or prior outputs, so runs must be isolated through sequential cleanup or, preferably, parallel environments with separate state and telemetry. To ensure comparable results over time, PostHog is developing offline evaluations using resettable virtual machines seeded with carefully restored real-data slices, while addressing issues such as changing timestamps, missing data sources, privacy constraints, and agents recognizing synthetic environments. The account emphasizes iterative testing, trace inspection, realistic task design, and using different model families for detection and validation, noting that effective benchmarks depend on the particular model, harness, task, and organizational stack being evaluated.

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