Genuine question: why does AI memory still seem so underdeveloped?
I've been working on my own approach to this, and something keeps bothering me. Why are we still so constrained by context windows when we have all these other ways to store, retrieve, and manage information? I'm trying to build a system that adapts its working memory to what a task actually needs, moving relevant information in and out of context as needed instead of treating the context limit as the thing everything has to work around. I know RAG, summarization, compaction, and external memory exist. I'm not saying nobody is working on this. I'm wondering why there don't seem to be more mature systems that tie all of this together, especially for agents working on long-running coding tasks. Am I missing some obvious projects? Is this just a much harder problem than it looks, or are there other reasons this hasn't progressed further? I'm already building my own version, so I'd genuinely like to know what's out there before I reinvent something that's already been done. Also interested in hearing from people who've actually tried building this stuff, because I'm sure there are problems I haven't run into yet.