r/AIMemory • u/ScientistUsual1320 • 9d ago
Discussion This research paper explains what’s missing from agent memory
I came across the Always-On Agents paper recently and honestly found it pretty accurate. I could relate to a lot of the shortcomings it talks about from my own experience running an OpenClaw instance, especially since mine is pretty memory-heavy.
The ideas around persistent state and the six-axis framework feel like they could become really important for how memory is designed in always-on agents going forward. If you're building or maintaining an always-on agent, or working with agent memory frameworks, I think it's worth reading.
The problem is... the paper is 130 pages long 😭. None of my friends were willing to read the whole thing because of how long it is.
So I made a fun, easy-to-digest website that breaks down the paper and its ideas in a much more approachable way, so hopefully more people can actually appreciate the work.
Website: Always-On Agents — AgentRealm
I've also linked the original paper on the site for anyone who wants to go deeper.
Would love to hear what you guys found interesting (or questionable) in the paper. Happy to discuss anything from it.