Enterprise AI Memory at Scale

Most people assume AI agents remember things. They don't. LLMs are completely stateless, and the naive approach of replaying entire conversation histories becomes unsustainable at scale - burning through tokens, degrading performance, and costing tens of thousands daily. This talk exposes why common memory approaches (full context replay, basic RAG, accepting statelessness) all break under real-world enterprise conditions. You'll learn why memory needs to be treated as infrastructure - structured, persistent, and selectively retrieved - rather than raw text endlessly replayed. We'll explore the uncomfortable economics of context windows, why RAG alone isn't memory, and where compliance requirements make ad hoc solutions impossible. You'll discover how systems like Mem0 enable agents to accumulate experience across sessions, dramatically reducing costs while improving consistency and auditability.