From Approval Button to Control Plane: Implementing Human Review for AI Agents

This article provides a detailed technical guide on implementing human approval systems for AI agents in enterprise environments. It emphasizes moving beyond simple approval buttons to a full control plane architecture that includes policy enforcement, evidence collection, audit logging, and scope validation. The article covers anti-patterns like relying on agent prompts for approval, and proposes a safer design with an enforcement point (tool broker, orchestrator, or API gateway) that inspects tool calls, classifies risk, checks policy, routes approvals, and blocks unauthorized actions. It also details the 'approval package' concept, specifying what evidence humans should see (run IDs, workflow IDs, proposed actions, evidence fields) to make informed decisions. The content is directly relevant to IT/technology professionals working on AI agent safety, DevOps, and enterprise software architecture.