Beyond Semantic Similarity: Performance and Costs of Agentic Retrieval for Complex Tasks

Agentic retrieval using a ReAct loop outperforms standard dense retrieval by improving nDCG@10 by 8.7 points using identical embedding models. This method leverages LLM reasoning to navigate unstructured data, providing better generalization on out-of-domain tasks where specialized retrieval methods typically fail.