Benchmarking Agents in Insurance Underwriting Environments

2026-01-31

AI Summary

This work proposes UNDERWRITE, the first multi-turn insurance underwriting evaluation benchmark co-developed with domain experts to address the limitations of existing AI agent benchmarks that focus predominantly on open-domain tasks and rely on single accuracy metrics. UNDERWRITE incorporates real-world complexities such as proprietary business knowledge, noisy tool interfaces, and imperfect user simulations. Leveraging multi-turn dialogue modeling, hallucination detection, and pass^k evaluation, comprehensive assessments of 13 state-of-the-art models reveal significant performance gaps and fragilities of general-purpose agents in specialized enterprise settings. Notably, the highest-accuracy model is not necessarily the most efficient, and robust tool usage alone fails to fully suppress hallucinations, highlighting critical shortcomings for real-world deployment in high-stakes domains like insurance underwriting.

Technology Category

• Multiagent Systems: Multiagent Systems under Uncertainty
• Reasoning under Uncertainty: Other Foundations of Reasoning under Uncertainty
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Search and Retrieval-Augmented AI: Agentic search

Abstract

As AI agents integrate into enterprise applications, their evaluation demands benchmarks that reflect the complexity of real-world operations. Instead, existing benchmarks overemphasize open-domains such as code, use narrow accuracy metrics, and lack authentic complexity. We present UNDERWRITE, an expert-first, multi-turn insurance underwriting benchmark designed in close collaboration with domain experts to capture real-world enterprise challenges. UNDERWRITE introduces critical realism factors often absent in current benchmarks: proprietary business knowledge, noisy tool interfaces, and imperfect simulated users requiring careful information gathering. Evaluating 13 frontier models, we uncover significant gaps between research lab performance and enterprise readiness: the most accurate models are not the most efficient, models hallucinate domain knowledge despite tool access, and pass^k results show a 20% drop in performance. The results from UNDERWRITE demonstrate that expert involvement in benchmark design is essential for realistic agent evaluation, common agentic frameworks exhibit brittleness that skews performance reporting, and hallucination detection in specialized domains demands compositional approaches. Our work provides insights for developing benchmarks that better align with enterprise deployment requirements.

Problem

• AI agent benchmarking
• insurance underwriting
• enterprise AI evaluation
• real-world complexity
• domain-specific hallucination

Innovation

• enterprise benchmarking
• insurance underwriting
• expert-in-the-loop
• tool-augmented agents
• hallucination detection