“The hardest part about memory is actually not the database at all. When we talk to a number of companies, the way that they do memory is they just store it in a single file. Very, very kind of simple. The hard part is taking all of the observations that happen … and distilling that into information that's valuable enough to go into this context layer.”

Because it's tricky to get LLMs to do what you want them to do. And this type of like meta-reflection, I think, is something that LLMs actually still struggle at a lot, like reflecting on kind of like prompts and improving prompts, reflecting on agent architecture, improving agent architecture. We should build a benchmark for this. Internally, we do have some benchmarks for this in terms of Engine, which is our agent that's running on things. But I think this type of meta-reflection is still really difficult for agents. I think it's also generally like a hard thing for humans to do as well. Like, I don't know if we actually understand, some people probably do, so I shouldn't say this, but I don't know if we understand like what goes on inside our brain. Like, I feel like our brains are still kind of like complex things that we don't fully understand. And so, if we're trying to now replicate this kind of like learning process in a system, I think there's a bunch of different pieces. And so, I think models aren't great at kind of like reflection. And I don't think we know what this system should look like. What should the inputs be?…