A Vision for LLM-based Interaction Assistance for Autonomous Agents on the Semantic Web

Advances in the Semantic Web and the Web of Things have enabled the dynamic advertisement of interaction descriptions, which allow autonomous agents to discover and reason about actions in hypermedia environments. However, these descriptions occasionally fall short in open, dynamic settings—for example, they may contain incomplete knowledge about unexpected situations, or information that does not directly address the needs of heterogeneous agents. In practice, such gaps are typically bridged by humans, often relying on their common sense. In this paper, we envision the integration of Large Language Models (LLMs) into Hypermedia Multi-Agent Systems (MAS) to leverage their world knowledge to fill information gaps at run time and enable scalable support for agent interaction in open and dynamic hypermedia environments. Our proposal lays the groundwork for a framework that considers LLM-based assistive functions for interaction in Hypermedia MAS, enabling the parameterisation and contextual grounding of interaction, methods for agents to leverage this assistance, and safeguards to ensure integrity and transparency. With these ideas, our aim is to inspire further research exploring the extent to which LLMs can assist in managing semantic descriptions in hypermedia environments at run time, while balancing scalable interaction assistance with identified challenges.