📄 中文摘要
许多投入生产的 AI 代理实际上并不是代理，而是经过包装的聊天机器人，依赖于工具列表和运气。通过对客户支持、内部工具和实时消息平台中基于大语言模型（LLM）系统的构建与评审，发现了一个明显的模式。团队通常集成 LLM，连接几个 API 调用，然后称其为“代理”。然而，随之而来的延迟增加、上下文丢失和工具调用错误等问题，导致工程复盘时不得不反思：究竟出了什么问题？
大多数 AI 代理失败的原因及其正确设计方法
出处: Why Most AI Agents Fail (And How to Design Them Right)
发布: 2026年3月17日
📄 English Summary
Why Most AI Agents Fail (And How to Design Them Right)
Many AI agents deployed in production are not true agents; they are essentially enhanced chatbots relying on a list of tools and luck. After building and reviewing LLM-powered systems across customer support, internal tooling, and real-time messaging platforms, a clear pattern emerges. Teams often integrate an LLM, connect a few API calls, and label it an 'agent.' However, this leads to issues such as increased latency, breakdowns in context, and incorrect tool calls, prompting engineering post-mortems to question what went wrong.