📄 中文摘要
构建一个在演示中有效的 AI 代理相对简单，而在生产环境中可靠地工作则是完全不同的工程挑战。生产系统需要处理真实用户、真实数据以及在出现故障时的真实后果。生产代理架构必须应对真实数据的多样性，处理模糊和复杂的输入，并且能够支持多个代理实例的并发执行。设计时需要考虑各种失败模式和代码模式，以确保在实际应用中能够稳定运行。
构建在生产环境中真正有效的 AI 代理：我的技术方法
📄 English Summary
Building AI Agents That Actually Work in Production: My Technical Approach
Creating an AI agent that performs well in a demo is relatively straightforward, but ensuring it operates reliably in production presents a distinct engineering challenge. Production systems must accommodate real users, real data, and the real consequences of failures. The architecture for production agents must address real data variance, managing messy and ambiguous inputs, and support concurrent executions of multiple agent instances. It is essential to design around various failure modes and code patterns to ensure stable operation in practical applications.