OpenAI runs layered monitoring across its frontier models specifically to catch misalignment and unauthorized agent behavior before incidents escalate. The system does not wait for user reports. It watches model outputs continuously, flagging deviations from intended behavior at the inference level.
The architecture matters more than the headline. OpenAI separates detection into multiple tiers, meaning a failure at one layer does not blind the entire system. Responders receive alerts structured for intervention speed, not just logging. The paper details what triggers escalation versus what gets silently corrected, and that distinction is the technical core worth reading.
This is infrastructure documentation dressed as a blog post. If you build on OpenAI APIs, deploy autonomous agents, or work in AI safety, the operational specifics here are more useful than the abstract safety commitments the company usually publishes. Read it for the detection logic, not the conclusion.
[READ ORIGINAL →]