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Agentic AI 架构中的记忆、知识与上下文系统化方法
原标题：Article: Systemic Approach to Memory, Knowledge, and Context in Agentic AI Architectures
AI 摘要
InfoQ 发表系列文章，提出将 agentic AI 架构中的上下文、记忆与知识三者分离管理，并引入「上下文引擎」作为控制平面，负责在信息进入 LLM 推理端点前完成选择、过滤、排序、压缩与投递。文章认为上下文工程比提示词工程更关键，幻觉往往是上下文失败所致，应优化而非最大化上下文。
以上摘要由 AI 生成，可能存在误差。事实请以原文为准。
正文节选
Key Takeaways - Context engineering beats prompt engineering - the system that builds context matters more than the prompt itself. - Separate context, memory, and knowledge - each serves a different purpose and should be managed independently. - Balance history with real-time data - great agents combine experience with live signals. - Hallucinations are often context failures - better context leads to more reliable reasoning. - Optimize context, don't maximize it - small, relevant context outper
发布时间：2026-11-02 19:00
抓取时间：2026-11-02 19:04
来源机构：InfoQ