I designed a Harness to fix my agent's quality problem — then found 6 flaws in my own design

In my previous article (I tested 3 models as AI agent quality inspectors: the stronger the model, the more valid work it rejects - DEV Community), I measured three model tiers as agent output quality inspectors across 8 scenarios (4 valid, 4 garbage). The result was a clean precision-recall tradeoff:

qwen3:0.5b (weak model): 25% garbage pass-through, 50% false rejections

GLM-5.2 (strong model): 0% garbage pass-through, 75% false rejections

The honest conclusion: a quality gate isn't a solution — it's a risk-transfer layer. Each layer catches some failures and introduces new ones.

I didn't stop there. I asked myself: if you accept the human-in-the-loop cost and design a proper Harness — not an automatic fix, but a system that makes human review efficient — what does it look like?