Society Agent: A Hierarchical Multi-Agent Architecture with Autonomous Persistent and Ephemeral Agents and Persistent Evolving Knowledge

Background: Traditional AI coding assistants operate as single agents responding to immediate user requests, lacking persistence, organizational structure, and the ability to coordinate complex, long-running tasks. Existing multi-agent systems typically use ephemeral agents with flat architectures and no long-term memory. Objectives: We introduce Society Agent, a supervised multi-agent system that transforms standalone AI assistants into coordinated teams capable of autonomous, long-running work. The system's hierarchical architecture can model both human organizations (companies, departments, teams) and large software systems (modules, components, services). Methods: We design and implement a hierarchical agent architecture with persistent supervisors and ephemeral workers, integrating the Mind-Tool file-based memory system for persistent evolving knowledge. The system includes cron-based task scheduling, zero-token heartbeat monitoring, self-reconfiguration through folder reorganization, and a web dashboard for human oversight. Results: Our evaluation across three use cases (automated software development, organizational simulation, and self-reengineering systems) demonstrates that Society Agent successfully coordinates multiple agents across hierarchical departments while maintaining persistent knowledge and enabling autonomous task execution without continuous human intervention. Conclusions: Society Agent represents a paradigm shift from task-execution tools to organizational AI systems capable of modeling and eventually augmenting entire companies, departments, and development teams. The combination of hierarchical structure, persistent memory, and autonomous operation enables a new class of AI applications.