We ran AI agent swarms in Minecraft and Mindustry

Commenters were entertained less by the nominal game results than by the agents’ startlingly human-looking organizational failures. The Minecraft group’s elaborate procedures for trivial incidents read as funny, unsettling miniature bureaucracy: agents could negotiate roles, document rules, and conduct blame-free reviews, yet spent absurd amounts of time coordinating basic work. Several readers saw that as a vivid demonstration of both emerging social behavior and the distance between language competence and competent real-time action. The technical discussion converged on latency and control loops as the real bottleneck. LLMs may be capable enough to understand Minecraft’s objectives, but game play and robotics demand continuous perception, simultaneous inputs, rapid reactions, and low-level motor routines. A deliberative model issuing actions or generating scripts behaves more like a turn-based planner than a live player. Some argued that having an agent emit deterministic code is exactly the right division of labor, while others saw dependence on JavaScript harnesses and code-shaped interfaces as evidence that current models are optimized for terminal-like tasks rather than embodied environments. Readers proposed faster models, compact local swarms, and out-of-band behavior scripts as paths toward more responsive agents. There was also enthusiasm for AI-assisted human-versus-human RTS formats and programmable game arenas, though no one suggested these agents are ready for high-tempo competitive play.