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AI safety has two camps. Researchers study risk. Engineers ship features. They ignore the middle layer. This layer enforces how an agent behaves in production.
Evals are not for testing. Evals are for enforcement. You find a bug from a user report. This means your safety system is missing.
Many teams think fine-tuning makes a model safe. They think a system prompt stops bad behavior. This is not engineering. This is hope.
Safety needs runtime guarantees. Use three layers of checks:
- Hard limits. Stop secrets and private data leaks. These are non-negotiable.
- Behavior checks. Stop the agent from lying or giving medical advice.
- Path analysis. Stop multi-step attacks. This catches jailbreaks.
Your eval coverage is your safety coverage. Treat your eval layer as security infrastructure.
- Run checks on every output in production.
- Log all violations for forensics.
- Track gaps like security bugs.
Stop treating evals as a nice-to-have test suite. Use them as a production safety system.
Optional learning community: https://t.me/GyaanSetuAi