One of the frequently used multi-attribute decision making (MADM) methods is VIKOR, which provides a compromise solution with maximum group utility and minimum regret of decision makers with non-commensurable and conflicting attributes. In the literature, VIKOR method has been extended to the fuzzy case using type-1 fuzzy sets. Type-2 fuzzy sets let the fuzzy membership functions to be defined for providing us more design degrees of freedom and flexibility. In this paper, we developed an interval type-2 fuzzy VIKOR method including a fuzzy ranking method for interval type-2 fuzzy sets. Interval type-2 fuzzy weights for decision makers’ strategies have also been used in the proposed method. A sensitivity analysis has been applied to see the behavior of our decision with respect to the changing weights of maximum group utility strategies. We showed the applicability of the proposed method through an existent problem in the literature.

The vision of 6G requires autonomous, intelligent, and adaptive systems that can manage complex and dynamic communication environments. In this context, artificial intelligence (AI)-based agents are seen as key enablers, combining perception, knowledge, decision, and execution functions to operate across distributed network layers. This study provides a comprehensive overview of agentic systems for 6G, examining their architecture, core capabilities, and enabling AI techniques. We propose a novel framework for agentic intelligence levels which conceptualizes agentic AI systems as a gradual and multidimensional spectrum. Additionally, trust and governance levels, which characterize system deployability independently of agentic competencies, are introduced. We analyze how methods such as reinforcement learning (RL), federated learning, graph neural networks, transfer learning (TL), and meta-learning can be integrated into agents to support tasks including network optimization, predictive traffic management, spectrum allocation, energy-efficient resource control, and security enhancement. The potential of foundation model-based agents in 6G networks is examined, comparing them to classical RL-based agents to highlight their complementarity. Practical 6G use cases—including smart resource orchestration, environmental coordination, industrial applications, reliability and security assurance, and situational awareness—are presented to demonstrate the applicability of agents in real-world scenarios. Their strengths, limitations, and complementary roles within agent-based frameworks are highlighted. The current technological and industrial maturity levels of use cases are mapped to the proposed levels of agentic intelligence and trust/governance. The study bridges theoretical advances and practical 6G implementations by linking AI methods with concrete deployment challenges. Finally, the study identifies challenges related to scalability, interoperability, explainability, and trust, and highlights future directions for building reliable, adaptive, and ethically aligned agent-based systems in 6G.