Engineering Insights from Scaling an LLM Simulation to 800+ Persistent Agents

An independent developer published an engineering write-up detailing the architecture behind "Slow Vale," a persistent life simulation town hosting over 800 AI residents. The system manages 300–400 LLM calls per character daily with an average context of 30,000 tokens using hosted DeepSeek Flash inference. Operating real-time multi-agent environments at this scale demonstrates practical solutions for asynchronous execution, context caching, and cost control in production LLM applications. It provides a valuable architecture blueprint for developers building persistent, dynamic multi-agent worlds beyond simple prototype models. To prevent race conditions and invalid actions, the architecture strictly separates concurrent LLM inference from world-state mutations, treating LLM outputs as candidate actions that must pass state validation before execution. The decision context also dynamically separates long-term personality traits from fast-changing state variables such as hunger levels, spatial inventory, and nearby events.