Search

While traditional RAG relies on static vector lookups that often lose global context, agentic search transforms retrieval into a dynamic, reasoning-driven process. By utilizing hierarchical structures like RAPTOR, Knowledge Graph RAG and autonomous sub-agents, these systems can navigate complex, multi-hop queries that typically overwhelm standard semantic search. This shift from one-shot retrieval to iterative loops allows for parallelized processing and self-correction, ultimately providing the precision and structural awareness required for professional-grade document analysis. ...

As Large Language Models (LLMs) transition from simple chatbots to autonomous agents, the methods we use to feed them data must evolve. While Retrieval-Augmented Generation (RAG) remains the industry standard for grounding models in external data, its “vanilla” implementation—converting text chunks into vectors for semantic lookup—often falters when faced with interconnected documents, technical jargon, or multi-hop queries. For Machine Learning Engineers (MLEs) and Product Managers (PMs), understanding the shift toward Agentic Search is critical. This approach moves away from static lookups toward dynamic, iterative, and hierarchical strategies that mirror how a human expert navigates a complex knowledge base. ...