How I Built Open-Source Guardrails That Auto-Stop Runaway AI Agents

Runaway AI agents are expensive. Stories of agents burning through thousands of dollars overnight come up regularly on Reddit and Hacker News — no budget limit, no loop detection, no kill switch. The agent keeps calling GPT-4 in an infinite loop until someone wakes up and pulls the plug. I built reivo-guard to prevent this. It's an open-source guardrail library that detects and stops runaway AI agents — with sub-microsecond overhead. This post walks through the architecture decisions behind each detection layer. The Problem: Agents Don't Know When to Stop LLM agents fail in predictable ways: Infinite loops — The agent keeps asking the same question, or semantically similar variations Cost explosions — Token consumption spikes 100x with no warning Quality degradation — Responses get worse over time but the agent keeps going Cliff-edge failures — Everything works until 100% budget, then hard crash Among the tools I evaluated (Helicone, Portkey, LangSmith, Lunary, LiteLLM), most either ob