AI safety playbooks: evals become a standard practice (Oct 2025)

Oct 2025

“Safety” in 2025 is increasingly operational: teams want a repeatable way to measure risk and quality, not vague promises.

Evals matter because they turn debates into data. Whether you’re deploying agents or generative media, you need to know what fails, when it fails, and how you detect regressions.

The practical eval loop

- Define a small suite of real tasks (not toy benchmarks).

- Run it on every major update (model/provider/settings).

- Track deltas and investigate regressions.

This discipline also improves product quality: evaluation becomes a feedback loop that reduces “surprise” failures in production.