This paper introduces the Genesis Engine, a synthetic civilization infrastructure that generates empirical governance data by running constitutionally constrained agent populations across multiple independent large language model substrates. Each agent cognitive architecture is defined by a Mixture-of-Agents (MOA) genome — weighted blend ratios determining how multiple LLM providers contribute to a single agent reasoning. Agents discover their own identity, roles, and strategies through interaction under resource scarcity. Breeding crosses parental MOA genomes to produce offspring with novel cognitive architectures. The system produces publishable empirical evidence on cross-provider governance behavior, constitutional constraint effectiveness, and emergent institutional dynamics — data that no single model provider can generate independently. Substrate fleet: xAI Grok (reasoning and fast variants), Qwen (local edge), and cloud-routed models via Ollama.

Original research. No external citations. All frameworks, architectures, and methodologies referenced are the author own work.