Most AI agents built on Azure today are "knowledgeable"—they ground responses in enterprise data using standard RAG and rely on short-term, session-based memory. But once the session ends, the context disappears. The agent starts fresh, unable to recall prior interactions, user preferences, or previously established workflows.

Extending memory beyond a single session to create truly adaptive agents introduces a massive architectural challenge: the enterprise "RAM Wall."
This session introduces MemAnto, a newly launched open-source, deterministic agentic memory layer that solves the persistence problem on Azure. We will explore how MemAnto uses Information-Theoretic binarization (32x compression) to keep massive, user-scoped memory stores entirely within the CPU.