How I added human-in-the-loop approval to my AI agent in 5 minutes

I kept running into the same problem building AI agents: they'd take real-world actions: sending emails, creating tickets, deploying code, without asking first. The agent doesn't know the difference between a test environment and production. It doesn't know that "send follow-up email to all leads" means 3,000 emails. It just executes. So I built Queuelo - a dead simple approval layer you drop in front of any agent action. How it works Instead of your agent acting directly, it POSTs to Queuelo: curl -X POST https://queuelo.com/api/actions \ -H "Authorization: Bearer YOUR_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "action_type": "send_email", "summary": "Send follow-up to 3,000 leads", "risk_level": "high", "payload": { "template": "follow_up_v2", "count": 3000 }, "callback_url": " https://your-agent.com/webhook " }' Queuelo holds the action, emails you instantly, you approve or reject from a dashboard. When you decide, it fires the callback and your agent proceeds. What you