New agentic AI system for commercial insurance underwriting uses adversarial self-critique to challenge decisions and enhance reliability. It reduces hallucinations from 11.3% to 3.8% and boosts accuracy to 96% on 500 expert cases. Human oversight ensures accountability in regulated environments.
Key Points
- 1.Adversarial critic agent challenges primary decisions
- 2.Formal taxonomy of decision-negative failure modes
- 3.Human-in-the-loop for all binding decisions
Impact Analysis
Enables safer AI deployment in high-stakes regulated industries like insurance. Provides a model for responsible AI integration with human oversight. Bridges efficiency gains and accountability needs.
Technical Details
Decision-negative agentic system with bounded safety architecture. Evaluated on 500 validated cases via arXiv:2602.13213v1. Internal checks before human review.