The Dynamic Verifiable Multi-Agent Human Agentic Loyalty Loop (DVM-HALL) Model and the Net Human-Agent Score (NHAS) in Autonomous Commerce
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.
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- PossiblePossibly related (embedding) · 49%General Intuition’s $2.3B bet that video games can train AI agents for the real world →
- PossiblePossibly related (embedding) · 48%Learning to lead in a hybrid human-AI enterprise →
- PossiblePossibly related (embedding) · 47%Production-grade AI agents for financial compliance: Lessons from Stripe →
- PossiblePossibly related (embedding) · 46%Multi-agent social intelligence with Strands Agents and Amazon Bedrock →
- FuzzySimilar title/name (fuzzy) · 59%AgentCore-8B →
“Fuzzy title match (0.73): “The Dynamic Verifiable Multi-Agent Human Agentic Loyalty Loo” ≈ “AgentCore-8B””
- LinkedLinked via arxiv author · 85%Sai Srikanth Madugula →
“The Dynamic Verifiable Multi-Agent Human Agentic Loyalty Loop (DVM-HALL) Model and the Net Human-Agent Score (NHAS) in A”
- LinkedLinked via arxiv author · 85%Peplluis Esteva de la Rosa →
“The Dynamic Verifiable Multi-Agent Human Agentic Loyalty Loop (DVM-HALL) Model and the Net Human-Agent Score (NHAS) in A”
- LinkedLinked via arxiv author · 85%Daya Shankar →
“The Dynamic Verifiable Multi-Agent Human Agentic Loyalty Loop (DVM-HALL) Model and the Net Human-Agent Score (NHAS) in A”