The Shift from RAG to Agentic Memory: Optimizing Long-Context LLMs for Production Workflows
Introduction The past few years have witnessed an explosion of interest in retrieval‑augmented generation (RAG) as a way to overcome the limited context windows of large language models (LLMs). By pulling relevant documents from an external datastore at inference time, RAG can inject up‑to‑date knowledge, reduce hallucinations, and keep token usage low. However, as LLMs grow from research curiosities to core components of production‑grade workflows, the shortcomings of classic RAG become increasingly apparent: ...