How to Evaluate AI Agents in Production Before You Have Any Labeled Data

Developers deploying AI agents often face a 'cold-start problem': no labeled datasets or historical traces exist to benchmark agent performance from day one. A framework proposed by a developer on DEV Community argues that meaningful evaluation gates can be built without any labels by focusing on independent, unforgeable signals rather than costly model-as-judge scoring. The approach organizes checks into three tiers, prioritizing externally verifiable facts — such as whether a file exists, code compiles, or output is non-empty — over statistical baselines and AI-generated scores. Tier 1 and Tier 2 checks require zero labeled examples because they test whether an output is real, not whether it is good. The author warns that skipping these structural checks in favor of LLM-based scoring introduces circularity and leaves agents running ungated in production until failures become visible.