Orchestrating Emergent Storytelling with Embodied Multi-Agent Systems

We present a novel approach to emergent storytelling through multi-agent systems powered by large language models (LLMs), advancing beyond current approaches to game AI and interactive storytelling which rely on heavily scripted dialogue systems and moving closer towards genuinely emergent narrative ecosystems. Through two artworks / video games, , we demonstrate how LLM-driven agents with persistent memory, behavioral models, and coordination capabilities generate coherent narratives from simulated social dynamics. Our architecture introduces: (1) a hierarchical memory system integrating working memory, episodic buffers, and consolidated narrative storage; (2) a conversation graph that tracks topic centroids, engagement, and unresolved questions; (3) a hybrid orchestrator that directs autonomy by fusing LLM reasoning with the conversation graph; and (4) their integration within embodied agents with a streaming multimodal action-perception loop that enables spatial awareness and environmental responsiveness. Experiments reveal emergent behaviors including strategic deception, coalition formation, the spread of misinformation, and meta-narrative awareness. Our contributions include several architectural patterns for producing stable emergent narrative systems. Requests for name changes in the electronic proceedings will be accepted with no questions asked. However name changes may cause bibliographic tracking issues. Authors are asked to consider this carefully and discuss it with their co-authors prior to requesting a name change in the electronic proceedings. Use the "Report an Issue" link to request a name change. Do not remove: This comment is monitored to verify that the site is working properly