How we manage agentic memory is going to be key in scaling the future of agents. I'm curious how this scales with larger datasets? Like let's say an agent has to keep 5 parallel conversations, across 5 different social media acccounts, and each of the conversations is 10000 messages long. How would it manage parsing through huge DBs like that? or is it more for like more recent context?
Also, let's say an agent runs like 1000s of times, would each of those times become a version history?
I'm particularly interested in how parsing through agent context would work!