Agents don't replace vector search - they make it harder to get right
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.<br /> <br /> 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 refle [...]
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