"We have memory. We're using RAG."
You have retrieval. Those aren't the same thing, and the gap between them is where agents quietly go wrong.
Day 2.
RAG finds documents that look relevant to your question. That's it . That's the whole job. It's a very good search engine bolted to a very good writer.
But consider what it can't do.
Your customer changed their pricing tier in March. The old contract is still in the index. The new one is too. RAG doesn't know which one is true, it just knows both are relevant. It hands the model two answers and lets it guess.
Memory would know one of those facts replaced the other, and when.
That's the difference. Retrieval finds. Memory concludes.
Retrieval asks "what documents match?" Memory asks "what do I actually believe, what changed my mind, and when did that happen?"
One is lookup. The other is position.
This matters because a system that only retrieves can never be wrong and it can never be right either. It has no beliefs to correct. Every contradiction in your data is a contradiction it will faithfully pass along, forever, with total confidence.
Day 3: if memory means concluding things, then something has to decide what gets remembered. Right now, in most systems, nothing does.
We at AlphaNimble are building Memuron, a memory system for AI agents. This series is thinking behind it, in the open.
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