AI代理的系统调试：引入AgentRx框架

As AI agents evolve from simple chatbots to autonomous systems capable of managing cloud incidents, navigating complex web interfaces, and executing multi-step API workflows, the challenge of transparency has emerged. When humans make mistakes, their logic can usually be traced. However, when an AI agent fails—perhaps by hallucinating a tool output or encountering other issues—tracing its decision-making process becomes complex. To address this challenge, the AgentRx framework has been introduced, aiming to provide a systematic debugging approach to enhance the interpretability and reliability of AI agents. By analyzing the behavior of AI agents, the framework assists developers in identifying and rectifying potential errors, thereby improving system transparency and user trust.