How I made AI agents safe to run on real infrastructure

Short summary

Mark Korotin shares how Cmdop ensures AI agents safely execute on real infrastructure by validating output as structured contracts, logging full traces, and scoring unintended side-effects. The core principle: autonomy is earned through measurement and data, not confidence. By incrementally moving actions from human-in-the-loop to autonomous after demonstrating safety with eval data, he transformed a demo into production-ready infrastructure.

• •Structured output validation prevents malformed or out-of-policy actions before execution
• •Side-effect scoring catches unintended consequences that task-completion metrics miss
• •Autonomy is earned step-by-step through eval data, not granted by prompt quality

Generated with AI, which can make mistakes.