From Data to Vectors to Context: What AI Agents Need Beyond RAG

​Ask a data agent for projected Q4 mobile growth. The SQL is correct. The forecast is wrong, because a one-day partner promotion was counted as demand. The explanation already existed in a campaign brief in Drive, a Slack thread, a Salesforce offer code, and an analyst's Databricks review. Retrieval can find all four. Nothing tells the agent which of them to trust or what it is allowed to act on.

​Join Jörg Schad, VP of Engineering at Pinecone, for a 60-minute session on what changes when agents act on the data they retrieve. He'll trace how retrieval got here, from keyword search to vectors to RAG. Then he'll lay out what agents need next: context that is current, trusted, and permitted for the task.

​What You'll Learn

• ​A working definition of context as a contract, with five parts: semantics, authority, policy, freshness, and provenance

• ​Why precomputed context works like a materialized view in a database, where keeping it current is the hard part

• ​How Pinecone Nexus, a knowledge engine, approaches this today, including what is still in progress

• ​How to tell when an LLM alone or RAG you build yourself is a better fit than Nexus

​Live Q&A with Jörg follows the talk. Can't make it? Register and we'll send you the recording.

​When: Wednesday, November 4, 11:00 AM to 12:00 PM PT / 2:00 to 3:00 PM ET