Pinecone is the knowledge infrastructure for AI at scale.

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  Nexus is GA. Enterprise knowledge, compiled once, query-ready for agents. On Sierra's τ-Knowledge: GPT-5.2 +12% accuracy, -80% cost. GPT-5.5 same accuracy, -77% cost. Read the full announcement: pinecone.io/blog/pinecone-…

  

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  API design has always had one reader in mind: a human developer. However agents are the new reader that looks at things a bit different. Here are some of the tenets that we now hold our API to: 1. Errors are guidance 2. Budget the reader's context 3. Self-description beats

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  Most semantic search queries leave something unstated. The user knows what they mean. The system doesn't. Pinecone's text match filters scope a vector search to a lexical condition (a machine number, a jurisdiction, a country) before the search runs, so an agent gets the right

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  Knowledge is rather abstract and philosophical. So what does it mean from an agentic context? How does providing the right knowledge at the right time to an agent increase its accuracy, task completion, and cost efficiency far above and beyond a frontier model alone? Watch the

  

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  Building Agent Skills and testing them is hard, but it doesn't have to be. Listen to Arjun Patel demo Cultivar, an open source tool developed at Pinecone to help benchmark agent skills in sandboxes. Thanks to

  @Voxel51

  for hosting us!