RAG

Traditional Retrieval-Augmented Generation (RAG) is failing to meet the demands of the agentic AI era. Designed for human-scale query patterns, RAG’s inference-time architecture is collapsing under the weight of agentic workloads, leading to massive compute waste, high latency, and unreliable results. Discover why the industry is shifting toward a ‘compilation-stage knowledge layer’—a move that replaces brute-force retrieval with persistent, pre-compiled, and auditable knowledge artifacts to unlock faster, cheaper, and more deterministic AI performance