New agentic AI system for commercial insurance underwriting uses adversarial self-critique where a critic agent challenges primary decisions before human review. It reduces hallucinations from 11.3% to 3.8% and boosts accuracy from 92% to 96% on 500 expert cases. The human-in-the-loop design ensures oversight in regulated environments.
Key Points
- 1.Adversarial critic reduces AI errors significantly
- 2.Human authority over all binding decisions
- 3.Taxonomy of failure modes for risk management
Impact Analysis
Enables safer AI deployment in high-stakes regulated industries like insurance. Provides a model for responsible AI integration with indispensable human oversight. Bridges efficiency gains with reliability needs.
Technical Details
Decision-negative agents with bounded safety architecture. Evaluated on 500 expert-validated underwriting cases. Formal taxonomy characterizes critic agent failure modes.