This article introduces a dynamic retrieval routing pattern for Retrieval-Augmented Generation (RAG) systems, particularly for agentic applications. Unlike traditional RAG that performs a single retrieval step, this pattern integrates retrieval into the agent's decision loop, allowing for multiple, strategy-specific retrieval calls. The proposed architecture includes a Retrieval Router that classifies queries to select the optimal retrieval method, such as vector search, BM25 keyword matching, web search, or a hybrid approach. This dynamic approach enables agents to refine their queries, switch retrieval strategies mid-task, and conditionally retrieve information only when necessary, improving performance on complex, multi-hop questions. AI
IMPACT Enhances agentic AI systems by enabling more sophisticated and context-aware information retrieval, improving performance on complex queries.
RANK_REASON The item describes a pattern for implementing RAG systems, which is a technique rather than a new product or frontier release.
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