The control-plane / data-plane split is borrowed from networking and infrastructure. The data plane is where the work happens — in an agent system, that is each agent executing tasks: reading files, running tests, opening pull requests. The control plane is the layer that decides which agents exist, what they are allowed to do, when they run, and how their behavior is recorded. Keeping the two separate is what lets an organization scale from a handful of agents to a managed fleet without losing oversight.
A control plane answers operational questions that no single agent can answer about itself: Which agents are registered, and who owns each one? What is each agent's role and scope? Is an agent currently running, idle, or quarantined? What has each agent done, and under which governing rules? Without a control plane these answers are scattered across laptops, shell scripts, and individual API dashboards — the condition that produces shadow AI and agent sprawl.
In practice a control plane provides agent registration and identity, lifecycle commands (start, stop, schedule), policy and governance (roles, approval gates, run-time budgets, risk controls), and observability (a shared audit trail and status view). It is the difference between running agents and managing a fleet of them.