Agentic Harnesses: LLM-Driven Verification Layers for Robot Autonomy
This work proposes a LLM-driven verification layer between planning and execution to evaluate action permissibility, and achieves near 85% precision across accept/escalate/reject categories, with negligible errors between accepting and rejecting tasks, and errors mostly manifesting at the escalate boundary.