How to Build a Control Plane for AI Agents

The article presents a structured 9‑step methodology for constructing a control plane that governs the actions of large language models acting as autonomous agents. It details how to define granular permission scopes, enforce policy checks before any LLM output is executed, and log every decision for auditability. The framework balances safety and flexibility, adding a modest latency overhead for policy evaluation while enabling rollback of unsafe actions. It concludes by illustrating how to embed the control logic as a thin wrapper around standard L

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