The moat is eval infrastructure, not the model

A Towards Data Science framework distilled from 100+ deployments argues the teams shipping agents successfully aren't the ones with the best model — they're the ones with the best evaluation harness. Key specifics worth adopting: measure all-runs consistency with pass^k (not just pass@k), calibrate your LLM judge against a human gold set, grow your eval datasets from real production traces, and gate CI on actual scores. It also stresses logging full trajectories — every prompt, tool call, and intermediate thought — so failures are debuggable.

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Sources: Towards Data Science · Arize comparison