The evals crisis — how do you trust your metrics?

Public benchmarks are saturated and possibly contaminated; private evals are expensive and hard to keep fresh. We're flying partially blind on model selection. How are serious teams building evals they trust in 2026 — synthetic data, human panels, adversarial sets? What's your process?