Researchers demonstrated a visual verification mechanism that enables autonomous agents to correct policy errors at inference time without model retraining. The system uses visual feedback loops to detect and steer agent behavior during execution, allowing real-time policy adjustment. For operators managing autonomous systems, this eliminates the retraining cycle for policy corrections. Rather than collecting failure cases, retraining on new data, and redeploying—a process requiring weeks and computational overhead—operators can now patch behavioral errors during runtime. This reduces the feedback loop from deployment-to-fix from weeks to seconds. The operational shift is material: safety-critical autonomous deployments can implement corrective measures without interrupting service or incurring retraining costs. This moves the constraint from model capability boundaries to feedback signal quality. Teams will invest in robust visual monitoring and verification infrastructure rather than larger training runs. The infrastructure plays become validation systems and real-time steering pipelines, not model scaling or dataset expansion.