Agents need vector search more than RAG ever did

What's the role of vector databases in the agentic AI world? That's a question that organizations have been coming to terms with in recent months. The narrative had real momentum. As large language models scaled to million-token context windows, a credible argument circulated among enterprise architects: purpose-built vector search was a stopgap, not infrastructure. Agentic memory would absorb the retrieval problem. Vector databases were a RAG-era artifact.

The production evidence is running the other way.

Qdrant, the Berlin-based open source vector search company, announced a $50 million Series B on Thursday, two years after a $28 million Series A. The timing is not incidental. The company is also shipping version 1.17 of its platform. Together, they reflect a specific argument: The retrieval problem did not shrink when agents arrived. It scaled up and got harder.

"Humans make a few queries every few minutes," Andre Zayarni, Qdrant's ...