Can LLMs play chess or not? In this talk, we use Kafka Queues (KIP‑932) to orchestrate agents playing chess without worrying about topic sprawl, partition limits, or LLM latency (which wrecks real‑time pipeline performance). We build Multiverse Chess: the white player is a share group that processes games in parallel and responds across many simultaneous boards. Its challengers (expert, random, greedy, troll, etc.) reply as soon as white plays. We track queue depth, per‑move latency, and scale to stress-test the system. This is a fun, technical demo of how Kafka Queues scale agentic workflows without partitioning issues. It shows how autonomous agent systems will rely on streaming platforms to manage LLM latency and operate effectively at scale.