Recently @pinecone introduced Nexus – a new knowledge-engine layer for AI agents that reduces token use by up to 90%. It’s built on top of a…

Recently @pinecone introduced Nexus – a new knowledge-engine layer for AI agents that reduces token use by up to 90%. It’s built on top of a vector database, but shifts reasoning earlier in the pipeline: from retrieval at query time to knowledge compilation before the agent even asks for information. Instead of giving agents raw files, Nexus prepares task-optimized representations in advance. These representations include artifacts – structured forms of information tailored to each agent’s specialization and workflow. For example, a finance agent gets only billing schedules, pricing rules, and usage thresholds. Pinecone’s VP of Product Jeff Zhu explains it like this: "The artifact representation can range from markdown files to extracted entities to tables. These artifacts are then indexed for retrieval within Pinecone’s database which support both semantic, sparse, and full text search capabilities so that the right artifacts are retrieved during query time and then composed for the final structured output response from Nexus." So vector databases are evolving from passive storage layers into active knowledge engines for agents. Read this to learn what distinguishes vector database in the Agentic Era: https://www.turingpost.com/p/agentic-vector-databases

Source: Pinecone (X) | 2026-05-17