思考 AI 程式碼代理的記憶機制
作者探討了目前用於 AI 程式碼代理的提示（prompts）和規則（rules）機制的局限性，並提出了一個獨立的「記憶」層，用於儲存原子化的知識片段。文章強調了模糊記憶、上下文污染以及在管理記憶時仍需人類判斷等挑戰。
Things like validating input, being careful with new dependencies, or respecting certain product constraints. The usual solutions are prompts or rules.
After using both for a while, neither felt right.
- Prompts disappear after each task.
- Rules only trigger in narrow contexts, often tied to specific files or patterns.
- Some principles are personal preferences, not something I want enforced at the project level.
- Others aren’t really “rules” at all, but knowledge about product constraints and past tradeoffs.
That led me to experiment with a separate “memory” layer for AI agents. Not chat history, but small, atomic pieces of knowledge: decisions, constraints, and recurring principles that can be retrieved when relevant.
A few things became obvious once I started using it seriously:
- vague memory leads to vague behavior
- long memory pollutes context
- duplicate entries make retrieval worse
- many issues only show up when you actually depend on the agent daily
AI was great at executing once the context was right. But deciding what should be remembered, what should be rejected, and when predictability matters more than cleverness still required human judgment.
Curious how others are handling this. Are you relying mostly on prompts, rules, or some form of persistent knowledge when working with AI coding agents?
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