Agentic RAG Adds Decision Loops to Retrieval

ByteByteGo breaks down how agentic RAG differs from standard retrieve-and-generate pipelines by inserting evaluation checkpoints between retrieval and response. The agent decides whether results are relevant, rewrites ambiguous queries, and routes to different knowledge sources – SQL databases for financial queries, document stores for policy lookups. Practical, but the trade-offs are real: 3-10x cost increase, 10+ second latency versus 1-2 seconds for basic RAG, and what the piece calls the evaluator paradox – using an LLM to judge its own retrieval quality can compound the same weaknesses. Most teams building RAG systems right now are hitting exactly this ceiling where simple pipelines fail on multi-document or ambiguous queries but full agentic loops feel expensive to justify.