📄 中文摘要
AI 代理在演示中表现完美，能够处理测试用例并优雅响应，令团队印象深刻。然而，实际部署后，常常会遇到未处理的边缘案例、依赖中的安全漏洞以及代理之间的协调失误等问题，导致生产环境崩溃。这种情况在 80% 的 AI 代理部署中都能见到。代理在干净数据上训练，但在复杂的真实世界输入中却可能失败，尤其是那些在测试中未曾出现的边缘案例。因此，进行预发布验证是确保 AI 代理在生产环境中稳定运行的关键。通过识别潜在的问题并进行充分的测试，可以显著降低生产环境中的故障风险。
为什么你的 AI 代理在生产环境中会失败（以及如何验证它不会）
出处: Why Your AI Agent Will Fail in Production (And How to Verify It Won't)
发布: 2026年3月23日
📄 English Summary
Why Your AI Agent Will Fail in Production (And How to Verify It Won't)
AI agents often perform flawlessly during demos, effectively handling test cases and impressing teams. However, once deployed in production, they frequently encounter unhandled edge cases, security vulnerabilities in dependencies, and coordination failures between agents, leading to system breakdowns. This pattern is observed in 80% of AI agent deployments. Agents trained on clean data can struggle with messy real-world inputs, particularly edge cases that were not present during testing. Therefore, pre-launch verification is crucial for ensuring the stability of AI agents in production. Identifying potential issues and conducting thorough testing can significantly reduce the risk of failures in production environments.