A Multi-MCP Governance Architecture for Persistent Artificial Worlds

This preprint presents a theoretical framework for a Meta Controller Processes (MCP) governance architecture in which multiple supervisory agents collaboratively design, regulate, and evolve a simulated artificial world populated by autonomous learning entities. The work explores how distinct supervisory roles corresponding to creation, preservation, and transformation can be formalized to manage model generation, environmental structure, and lifecycle control within large-scale agent-based simulations. Rather than focusing on implementation details, the paper emphasizes conceptual design principles, governance separation, and system-level constraints for scalable artificial societies. It examines how supervisory agents might coordinate resource allocation, model specialization, and world dynamics while remaining responsive to hardware limitations and computational budgets. This work is intended as a foundational, theory-driven contribution and a precursor to future empirical studies. No experiments are reported in this version. Subsequent work will investigate practical implementations, evaluation methodologies, and emergent behaviors arising from such architectures.