Governing the Swarm: Config-Driven Control Plane for Human-in-the-Loop Multi-Agent Systems at Scale
Most "AI agent" demos die at the second agent. A single agent answering a single question is a solved problem; an organization of agents that continuously watch the world, propose actions, wait for human approval, execute, and learn from the outcome is not.
The hard part was never the model - it is coordination, authority, and scale.
This article presents a production-shaped reference architecture for a Human-in-the-Loop control plane: a small, frozen "engine" of three generic services connected by a shared publish/subscribe fabric, where every new capability is added as configuration and plug-ins, not new plumbing. The design lets one human safely supervise many concurrent, heterogeneous agent sessions from a single pane of glass, and it scales by adding threads and topics, not by complicating any single conversation.
We describe the full closed loop - detect → propose → approve → execute → record → learn - the message contracts...
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