Source: Nate’s Substack
The bottleneck in current AI agent design isn't vector search itself but the architectural inefficiency of re-embedding and re-retrieving identical context across sequential runs—a waste that compounds in multi-step reasoning tasks. Agents need persistent, indexed memory layers that survive between executions rather than treating each decision as a cold start. The vector search debate misses the point because it's treating retrieval as a stateless lookup problem when the actual challenge is maintaining state across an agent's reasoning trajectory. That shift changes how RAG systems are built and the economics of inference at scale.