Why AI agents need more than a vector database: Context Packs, AQL, verification, and evidence-aware context.
I'm building CortexDB — an agent-native context database for AI agents
Most modern RAG systems follow the same pattern:
Split documents into chunks
Compute embeddings
Store them in a vector database
Key Takeaways Most SaaS AI agents don't need a vector database — file-based memory plus 1M-token...
Learn what a context engine is, how it fits into agent architecture, where RAG falls short, and how Redis powers the context…
Watch now | There’s a debate going on right now about whether vector search is obsolete.
Learn how context retrieval works in AI agents, why basic RAG fails at scale, and how Redis supports reliable retrieval with…
TL;DR: AI coding agent memory should live in the repository, not the chat window. Bigger context...
RAG retrieves documents but not decision logic, causing agents to act on expired rules. Decision context graphs encode…
