When Agent Automation Becomes Profitable: Quantifying and Insuring Autonomous AI Risk through Trace-Economic Underwriting

The paper introduces "trace-economic underwriting," a methodology for quantifying and transferring the risk associated with autonomous AI agents in operational systems. It emphasizes mapping tool-use traces to customer exposure and claimable losses, which allows for more accurate pricing and risk management, achieving a significant reduction in pricing mean absolute error (MAE) from $17.7K to $569. This approach is crucial for practitioners as it provides a framework to economically justify the deployment of AI agents, balancing expected benefits against risks and costs, thus facilitating broader adoption of automation in high-stakes environments.