Net Human-Agent Score

DVM-HALL model and Net Human-Agent Score proposed for AI agent loyalty dynamics in autonomous commerce

A preprint from arXiv introduces the Dynamic Verifiable Multi-Agent Human Agentic Loyalty Loop (DVM-HALL) model, a theoretical framework for understanding brand loyalty when AI agents autonomously execute purchasing decisions on behalf of humans. The model formalizes brand selection via a softmax formulation incorporating emotional equity, agentic utility, trust, delegated authority, and verifiable execution, with recursive trust-updating mechanisms. It also introduces the Net Human-Agent Score (NHAS), a risk-weighted metric for measuring human-agent alignment using feedback logs and verifiable receipts. The framework extends into DeFi and tokenized loyalty settings, treating execution risks like gas costs and MEV exposure as predictors of agentic brand preference.