Governance frameworks for AI agents in production: layered review, risk classification, compliance, runtime auditing, and the prerequisites for scaling AI safely.
Without mature governance, AI agents never leave the experimentation phase. pymnts.com summarizes it: governance gives agents "permission to grow." Without review layers, risk classification, and auditing, any production failure becomes an incident that halts the entire operation.
Start by classifying changes by risk level (as Lovable did). Define where humans approve and where the machine decides on its own. Implement runtime evidence (like Halo) for auditing. Establish scope limits for each agent. And document every decision for compliance.
The GitLost case showed agents leaking private repositories when reading untrusted content. Claude Code had a possible session leak between accounts. Models can invent fields when calling tools. The main risk is treating agent output as trusted without validation, exposing data and systems.