we deployed an ai assistant for our sales team about 6 months ago. The feedback was weirdly consistent. its smart but it doesnt really learn. every conversation mostly starts fresh. It doesnt retain much about past customer interactions, patterns that led to successful deals, internal product knowledge from earlier calls, or team workflows that actually work. We tried the obvious stuff. Longer context windows help a bit but get expensive fast. RAG over past conversations improves retrieval but it still feels reactive instead of cumulative. Fine tuning is possible but iteration cycles are slow and messy when sales feedback changes weekly. what seems to be missing isnt raw model capability. Its some kind of structured memory layer that consolidates interactions into higher level learnings and updates over time without retraining the base model. After talking with a few other teams this doesnt seem unique to us. A lot of enterprise deployments hit a stateless ceiling sooner than expected. Theres apparently an $80k competition happening right now (Memory Genesis Competition 2026) focused specifically on long term memory for agents. Interesting signal that this area is starting to get more serious attention. maybe the next step for enterprise ai isnt scaling parameters again but figuring out how agents accumulate experience instead of just responding turn by turn. submitted by /u/Ok-Line2658
Originally posted by u/Ok-Line2658 on r/ArtificialInteligence