Knowledge Engine Built for AI Agents
The knowledge engine for agents is quickly becoming a required layer for enterprise AI. Aaron Kao, VP of marketing at Pinecone, joins Alan Shimel on Techstrong TV. Furthermore, he explains why agents do not have a model problem, they have a knowledge problem.
About Aaron Kao
Aaron leads marketing at Pinecone after seven years at AWS and time at Pulumi with the platform engineering movement. In addition, he covers product marketing, dev rel, partnerships and the launch of Pinecone Nexus. Consequently, he brings a rare mix of cloud, developer and go to market perspective.
Why a knowledge engine for agents matters
Aaron shares how Pinecone pioneered vector databases and retrieval augmented generation back in 2019. Meanwhile, more than one million developers and ten thousand organizations now build on Pinecone. Therefore, the team saw the agentic shift very early through API usage patterns.
He explains why agents change everything, from token maxing to the reverse information paradox. Furthermore, autonomous multi step retrieval quickly blows through token budgets. As a result, a purpose built knowledge engine for agents becomes an enterprise priority.
Inside Pinecone Nexus
Aaron describes Nexus as a knowledge engine for agents that pre compiles knowledge artifacts from many sources. In addition, this offloads runtime search work that agents used to redo on every single call. Consequently, teams get up to ninety seven percent lower token costs and much faster responses.
He walks through how Nexus deploys inside a customer’s own cloud with any model choice. Meanwhile, data never leaves the governance boundary and open weight agents fit in cleanly. Therefore, the knowledge engine for agents supports both frontier models and self hosted stacks.
Who needs this today
Aaron argues that any enterprise running agents at scale, off the shelf or open weight, will eventually need this layer. Furthermore, platform teams building agents for business users feel the pain first. Consequently, Nexus is aimed at those large scale agent deployments.
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