📄 中文摘要
传统的监控方法主要基于简单的合约：系统要么正常工作，要么出现故障。然而，AI 代理打破了这一合约。即使代理完全可用，没有崩溃、超时或错误代码，它仍可能产生错误的答案、调用错误的工具或虚构信息。在基础设施的角度来看，一切看似正常，但从用户的角度来看，代理却是失效的。生产中最大的事故往往是无声的失败，这使得监控 AI 代理变得更加复杂。有效的监控不仅需要关注正常运行时间，还需要追踪代理的输出质量和决策过程，以确保其在实际应用中表现良好。
如何监控生产中的 AI 代理
出处: How to Monitor AI Agents in Production
发布: 2026年3月9日
📄 English Summary
How to Monitor AI Agents in Production
Traditional monitoring relies on a straightforward contract: the system either works or it doesn't. However, AI agents disrupt this contract. An agent can be fully operational—no crashes, timeouts, or error codes—yet still produce incorrect answers, invoke the wrong tools, or fabricate information. From an infrastructure standpoint, everything appears healthy, but from a user perspective, the agent is malfunctioning. The most significant incidents in production often stem from silent failures, complicating the monitoring of AI agents. Effective monitoring requires not only tracking uptime but also assessing the quality of the agent's outputs and decision-making processes to ensure optimal performance in real-world applications.