Agentic RAG for Personalized Recommendation
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First page
The curator’s take
Key pointsIntroduces a multi-agent framework that enhances traditional RAG systems with reasoning agents tailored to user modeling and contextual ranking. Developed at Walmart Global Tech, ARAG reframes recommendations as a structured coordination problem between LLM agents.
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User Understanding Agent synthesizes user preferences from long-term and session behavior.
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NLI Agent evaluates semantic alignment between candidate items and user intent.
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Context Summary Agent condenses relevant item metadata.
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Item Ranker Agent ranks final recommendations using all prior reasoning.
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