I'm building CortexDB — an agent-native context database for AI agents Most modern RAG systems work like this: Split documents into chunks Generate embeddings Store them in a vector database Retrieve top-k similar chunks on query Send them to an LLM It works for simple use cases. But as AI agents become more autonomous and complex, a clear problem appears: Agents don’t just need similar text chunks. They need bounded, permission-safe, evidence-aware, and verifia...