The article introduces the Dynamic Verifiable Multi-Agent Human Agentic Loyalty Loop (DVM-HALL) model to address how traditional customer loyalty paradigms fail when AI agents execute purchasing decisions autonomously. It proposes a framework where brand choice is determined by human emotional equity, agentic utility, calibrated trust, and verifiable execution risks in decentralized finance environments.
- The DVM-HALL model uses softmax probability formulation to jointly determine selection based on human emotional equity, machine-experience utility, and delegated authority.
- It features recursive updating mechanisms to dynamically calibrate trust and delegation after each interaction.
- The framework integrates a verifiable execution layer for DeFi and tokenized loyalty, incorporating risks like gas costs, slippage, MEV exposure, and smart-contract vulnerabilities.
- The authors introduce the Net Human-Agent Score (NHAS), an auditable, risk-weighted metric measuring human-agent alignment using feedback, logs, and verifiable receipts.
- A three-stage empirical validation plan is proposed, spanning controlled shopping experiments, multi-agent market simulations, and DeFi testbeds.
This framework provides the foundational theory required for brands to navigate the transition toward machine customers.