Forkcast: How Pro2Guard Predicts and Prevents LLM Agent Failures
If your AI agent is putting a metal fork in the microwave, would you rather stop it after the sparks fly—or before? That’s the question Pro2Guard was designed to answer. In a world where Large Language Model (LLM) agents are increasingly deployed in safety-critical domains—from household robots to autonomous vehicles—most existing safety frameworks still behave like overly cautious chaperones: reacting only when danger is about to occur, or worse, when it already has. This reactive posture, embodied in rule-based systems like AgentSpec, is too little, too late in many real-world scenarios. ...