Agents don't need memory, they need documentation

The piece argues RAG-style memory plugins fail because similarity search can't distinguish correct from stale information, and makes the case that structured documentation, not vector recall, is what keeps agents reliable on a changing codebase.

• RAG — Retrieval-augmented generation — fetching relevant documents first and pasting them into the model's context so it answers from your data instead of memory.
• AI agent — An AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.
• context window — The maximum amount of text a model can consider at once — its working memory for the current conversation or task.

• articleDetailed Guide to Agent Memoryvasa_
• articleFilesystem-Based Memory for LLM Agents: Organization, Evolution, and SustainabilitySizhe Zhou, Sheldon Yu, Hui Wei, Junda Wu, Siru Ouyang, Yizhu Jiao, Shijia Pan, Julian McAuley, Yu Zhang, Tong Yu, Jiawei Han
• postAI Memory Management PhilosophyMatt Van Horn

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