Researchers propose an outcome-grounded confidence calibration method for LLM agents to predict their own failure. They introduce a proper scoring rule and a separate evaluation protocol to assess the effectiveness of the method. The method improves the agent's confidence in predicting its success and reduces expected calibration error.