`AI agents are no longer just generating text. They're sending emails, pushing code, updating CRM records, and modifying databases. And when they go wrong, they really go wrong. I've seen this pattern repeatedly: an agent works perfectly in testing, gets deployed, and then sends 200 emails to the wrong list. Or deletes the wrong GitHub issues. Or overwrites 3 months of CRM data. The model didn't fail. The prompt was fine. There was just no safety net. The problem isn't the agent. It's the execution layer. Most teams handle this with logging. They add Langfuse or Helicone, watch the traces, and hope they catch mistakes before they happen. But logging tells you what went wrong after it happened. What you actually need is the ability to undo it. What reversible execution looks like The core idea is simple: before any action executes, you log it. After it executes, you store enough information to reverse it. If something goes wrong, you unwind in LIFO order.…