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

2026-06-15

AI Summary

This study addresses the challenge of deploying autonomous AI agents in high-stakes settings, where irreversible losses are often hindered by ambiguous accountability and unquantifiable risk. To overcome this, the authors propose a trajectory-economic underwriting framework that replaces subjective large language model judgments with deterministic economic labels derived from fine-grained tool-use traces mapped at the customer–task–trajectory segment level. This enables precise quantification of financial loss, facilitating granular risk assessment, equitable pricing, and insurability. Integrating loss annotations, conditional risk control, and actuarial modeling, the approach reduces CVaR95 by 72% on real-world SWE-smith trajectories, lowers mean absolute pricing error from \$17.7K to \$569, and achieves a 98.3% expert adoption rate for audited trajectory labels—substantially enhancing the economic viability of AI automation.

Abstract

AI agents can now take irreversible actions in operational systems, but agent-caused losses are still not clearly assigned, priced, or transferred. Providers often disclaim consequential damages, users are left with uncompensated losses, and default human review limits the efficiency gains of automation. We ask when autonomous AI deployment can become economically acceptable despite failure risk. Our answer is to quantify risk at the customer-task-trace episode level and transfer it through insurance. Automation is acceptable when its expected benefit exceeds the premium, control cost, and remaining risk. This requires a defined role with bounded permissions and comparable traces. We introduce trace-economic underwriting, which maps tool-use traces to customer exposure and claimable loss, then uses this representation for pricing, control, and risk transfer. It uses deterministic economic labels rather than an LLM judge. In our trace-to-loss testbed, trace-economic pricing reduces pricing MAE from $17.7K to $569 and removes regressive cross-subsidy. A 300-trace expert audit accepts 295 labels unchanged. On 1,000 real SWE-smith traces, trace-conditioned controls reduce CVaR95 by 72%. Theorem~1 gives a finite-sample scope condition. We release code, labels, and audit sheets.

Problem

• autonomous AI risk
• risk pricing
• liability assignment
• AI insurance
• trace-based underwriting

Innovation

• trace-economic underwriting
• autonomous AI risk
• tool-use traces
• risk transfer
• economic labeling