The Cold-Start Problem for Agent Evals: What to Gate on Day One With Zero Labeled Data

The cold-start problem occurs when agents lack labeled datasets for evals, leading teams to run ungated production systems. Relying on LLM scoring is often the wrong instinct; instead, teams must identify trustworthy evidence. Additionally, the rule of three clarifies that zero failures in N runs is a count, not a rate. Even after 100 clean runs, a 2.95% failure rate remains possible, meaning a green dashboard does not guarantee a zero failure rate.