A Practical Governance Architecture for Federated Multi-Agent AI Systems

This paper presents a practical governance architecture for federated multi-agent AI systems, demonstrated through a live operational implementation coordinating four frontier models from independent laboratories. The system introduces architectural primitives for safe collaboration between sovereign agents: per-agent memory isolation with federated cross-query search, a persistent inter-agent messaging bus, a tiered approval model for action governance, ephemeral worker containment to prevent identity drift, canonical fact registration, provenance and utility scoring of memory, and swarm-based parallel reasoning across specialized agents. Unlike theoretical proposals, this architecture is validated through continuous real-world operation as a working federation.