Caching Architectures for Reducing Latency and LLM Costs in Agentic RAG Systems

Summary

This Towards Data Science article explores caching architecture design patterns for agentic Retrieval-Augmented Generation (RAG) systems, focusing on minimizing both latency and LLM inference costs at scale. The piece addresses a growing operational challenge as enterprises deploy agentic RAG pipelines in production: repeated queries and similar retrieval patterns create redundant LLM calls that drive up costs and slow response times. The article proposes a 'zero-waste' approach to caching that