Today's concept: RAG vs Agentic rag vs multi agent rag
Three different systems get called RAG, and only one of them is the thing most people picture when they hear the word.
Plain RAG is a straight line. A question comes in, the system searches your documents once, pastes the best matches into the prompt, and the model answers from what it was handed. Same shape every time. A support bot that looks up your handbook and quotes it back is this.
Agentic RAG puts the model in charge of the searching. It can reword a query that came back empty, search again, pick a different source, or decide it has enough and stop. Not one lookup but a loop. When a coding assistant greps for a function, reads the file it found, then greps for something that file mentioned, that is agentic retrieval.
Multi-agent RAG splits the work across several of those loops, each with its own scope and its own context window, with a coordinator that fans out the sub-questions and merges what comes back. It earns its cost when the question is genuinely several questions (how did pricing, churn and support volume move last quarter) and no single pass would cover all of it.
Worth saying plainly: all three sit on the same retrieval underneath. Same index, same chunks, same search.
The insight I keep coming back to is that each step up trades a legible failure for a recoverable one. When plain RAG is wrong, you read the retrieved chunks and see exactly why. Agentic RAG can rescue its own bad first search, but the failure is now a trajectory instead of a snapshot. Multi-agent adds a failure neither of the others has: every agent retrieves correctly, and the merged answer still contradicts itself, because no one of them saw the whole picture.
Quick check before you scroll: If the same index sits under all three, what does moving from plain RAG to agentic RAG actually change about a bad retrieval, and what does it leave untouched?
Full breakdown + the answer: frankduah.me/learnings/2026-08-24-rag-vs-agentic-rag-vs-multi-agent-rag
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The answer
It changes the number of attempts, not the quality of what is there to find. The loop can reword its way into a hit the first query missed, but it cannot retrieve a document that was never indexed or was chunked badly, so a broken retrieval layer just fails several times instead of once, at several times the cost.