Agentic AI Architecture for Multi-Criteria Decision-Making: A Collaborative Human-AI Framework

Abstract

Group decision-making and negotiation increasingly take place in settings where stakeholders hold divergent objectives, values, and interpretations of evidence. However, Large Language Models (LLMs) integration in collective decision processes remains constrained by limited traceability, weak procedural control, and ambiguity regarding the role of human judgment. This conceptual paper proposes a reference architecture for agentic Artificial Intelligence (AI) in Group Decision and Negotiation (GDN) that integrates language-based reasoning with formal Multi-Criteria Decision-Making (MCDM) procedures. The architecture assigns two complementary classes of specialized agents to discrete stages of the process: generative agents, responsible for interpretative tasks such as problem structuring, criteria definition, and preference elicitation, and logical agents, responsible for deterministic operations including weighting, aggregation, and ranking. A human-in-the-loop (HITL) governance layer supervises tasks requiring subjective judgment or domain expertise, ensuring consistency, transparency, and auditability throughout the decision workflow. The primary contribution is a modular reference architecture, grounded in design science principles, that decouples generative interpretation from formal evaluation within a unified and auditable decision pipeline. The framework is illustrated through a representative multi-stakeholder scenario demonstrating the coordination of agents and human oversight across all stages of the MCDM process.