Building a solid Knowledge Agent? Start with Agentic RAG and hybrid search—that's your baseline.

The setup is pretty straightforward: chunk your data, run embeddings, then dump everything into a vector database. Next step? Equip your agent with a search tool that can query those vectors efficiently.

Want better accuracy? Try grouping your retrieval by domain first, then throw in a reranking layer. Makes a huge difference when precision matters.

Hybrid approach combines full-text search with semantic matching—covers both exact keywords and contextual understanding. That combo handles way more query types than going with just one method.

Vector databases aren't just storage anymore. They're becoming the memory layer for AI agents that actually need to reason over large knowledge bases. The reranking step filters out noise and surfaces what's actually relevant.

If you're serious about knowledge agents that don't hallucinate, this architecture is where you start.