Agent memory is a joke right now. How are you actually solving this in production?

Reddit r/AI_Agents News

Summary

A developer is asking the community how they actually solve agent memory in production, whether through vector DBs with RAG, knowledge graphs, or human-in-the-loop approval for a shared knowledge base.

Developers, how are you actually handling agent memory in production? We know agents can solve complex problems now, but they usually have the memory of a goldfish. Once the terminal session ends, that context is gone, and the next dev on your team has to start from scratch. How are you capturing what an agent learns so the rest of your team can actually leverage it? Are you just dumping chat logs into a vector DB and praying RAG finds it later? Building custom knowledge graphs? Or forcing a human-in-the-loop to approve the fix before saving it to a shared brain? I’m building something in this space with the community, and I want to know what patterns actually work for you right now, not just what looks good on a whiteboard. Let me know what you're using.
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