What are Chain-of-Agents and Chain-of-RAG?

What if the answer to LLMs struggling with long contexts isn’t a bigger context window, but a better way to move through information? This article explores two approaches from Google and Microsoft: Chain-of-Agents (CoA), where multiple agents pass insights along a sequential chain to process massive documents, and Chain-of-RAG (CoRAG), where retrieval itself becomes an iterative reasoning process. We break down how both methods tackle multi-hop reasoning and RAG’s limitations, where their performance gains come from, and why CoA may be better suited to extremely long contexts while CoRAG shines when complex questions require searching, refining, and retrieving information step by step.

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