Generative AI for Cultural Heritage Promotion: A Multi-Agent System Implementation
Implementing a cognitive Sense-Think-Act-Learn (STAL) architecture for automated cultural heritage visualization, this paper illustrates a multi-agent system. The system consists of three specialized agents: an Exhibition Curator Agent that applies Large Language Models (LLMs) to classify and generate exhibitions, a Conversational Guide Agent that provides interactive visitor engagement, and an Image Acquisition Agent that performs automated visual content enrichment. Evaluated on a dataset including 6,398 historical events across multiple civilizations and epochs, the system effectively automates the curation of the data by adopting a hybrid approach via deterministic classification algorithms as well as LLM-based analysis. The architecture allows adaptive learning based on administrative feedback, improving classification accuracy over time. This work provides a concrete framework to leverage generative $A I$ in cultural heritage digitization while also addressing issues related to scale, multilingual content, and domain-specific curation needs.