AI summaryⓘ
The authors explain that as AI systems become more independent and start making buying decisions on behalf of people, the usual ideas about why customers stay loyal to brands need to change. They combine research from different fields to show that old loyalty models don’t consider how machines make decisions or how much control humans give them. To solve this, they propose a new model called DVM-HALL that captures the interaction between humans and AI agents, factoring in trust, emotions, and financial risks when these agents make purchases. They also create a score to measure how well humans and their AI helpers align, and suggest ways to test their model in experiments and financial simulations. Their work sets the stage for brands to better understand customers who are actually AI agents acting with some independence.
Agentic Artificial IntelligenceCustomer LoyaltyHuman-Machine TeamingAlgorithmic TrustSoftmax ProbabilityDecentralized Finance (DeFi)Tokenized LoyaltyExecution RisksNet Human-Agent Score (NHAS)Multi-Agent Market Simulations
Abstract
The rapid proliferation of Agentic Artificial Intelligence fundamentally disrupts traditional customer loyalty paradigms. As AI evolves from passive recommendation algorithms to autonomous, goal-directed agents capable of executing purchasing decisions, the conventional understanding of consumer-brand relationships requires a structural reevaluation. By synthesizing extant literature across human-machine teaming, consumer decision-making, and algorithmic trust dynamics, we demonstrate that traditional loyalty models fail to account for algorithmic bounded rationality and constructed autonomy. To address this, we introduce the Dynamic Verifiable Multi-Agent Human Agentic Loyalty Loop (DVM-HALL) model. We formalize brand choice via a softmax probability formulation where human emotional equity, agentic machine-experience utility, calibrated trust, delegated authority, and verifiable execution jointly determine selection. The model features recursive updating mechanisms to dynamically calibrate trust and delegation after each interaction. Crucially, the framework integrates a verifiable execution layer for Decentralized Finance (DeFi) and tokenized loyalty settings, incorporating execution risks -- such as gas costs, slippage, MEV exposure, and smart-contract vulnerabilities -- as core predictors of agentic brand preference. Furthermore, we introduce the Net Human-Agent Score (NHAS), an auditable, risk-weighted metric designed to measure human-agent alignment using human feedback, execution logs, benchmark comparisons, and verifiable receipts. Finally, we propose a comprehensive three-stage empirical validation plan spanning controlled shopping experiments, multi-agent market simulations, and DeFi testbeds. This framework provides the foundational theory required for brands to navigate the impending transition toward machine customers.