CompanionCast: Reimagining Sports Streaming with Multi-Agent AI Companions

Abstract

Social presence is central to the enjoyment of live events, yet many fans watch sports alone. We investigate whether multi-agent conversational AI systems can recreate the dynamics of co-viewing and enhance immersion. We present CompanionCast, a prototype where multiple role-specialized AI agents (supportive, analytical, humorous) respond in real-time to sports events using caption streams, speech synthesis, and spatial audio. Distinctly, CompanionCast integrates an LLM-based evaluator agent that iteratively scores and refines conversations across five dimensions (relevance, authenticity, engagement, diversity, personality consistency). A pilot study with soccer fans suggests that multi-agent interaction improves perceived social presence compared to solo viewing, though delays and ASR issues limit fluidity. We contribute: (1) a framework for orchestrating multi-agent conversations around real-time multimodal streams, (2) a novel evaluator-agent pipeline for conversation quality control, and (3) exploratory evidence of increased social presence in AI-mediated co-viewing. We discuss challenges and future directions for generalizing this approach to broader event streaming and multimodal AI evaluation.