Pinecone, trusted knowledge infrastructure for AI, at Microsoft Build announced a new integration connecting Pinecone Nexus and Microsoft OneLake.This integration changes how enterprise AI agents get the information they need. It shifts the process from slow, expensive retrieval to fast, structured, cited responses built directly from data organizations already have in the Microsoft ecosystem.

Common AI agents spend the majority of their time and tokens trying to find relevant information in the data they have access to. They retrieve raw data, stitch it together, and send it to a frontier LLM to make sense of it. In production, at scale, this breaks down: task completion rates drop 60%, and token consumption and related costs become unpredictable.

Pinecone Nexus is a knowledge engine purpose-built for AI agents. Instead of making agents assemble raw data at runtime, Nexus does that work in advance, dynamically building structured, task-optimized contexts called artifacts. Each artifact is scoped to a specific task; it pulls the right data, applies the right permissions, and formats the result so the agent can use it directly.

Agents query artifacts through KnowQL, a query language purpose-built for knowledge retrieval. A KnowQL query specifies what the agent needs to know, the required output format, citation requirements, and latency budget. Nexus handles the rest. Early results show a 95%+ reduction in frontier LLM token usage, 30x faster task execution, and completion rates above 90%.

Organizations using Microsoft Fabric have already unified their data, including documents, tables, and Power BI semantic models, in OneLake. The Nexus integration connects directly to OneLake with no manual imports or upload steps. When an agent needs to complete a task, Nexus queries OneLake, builds an artifact scoped to that task and the user’s access permissions, and returns a structured, cited response through KnowQL. Every answer traces back to its source. No data is exposed beyond what RBAC permissions allow. PII is tagged at ingest and governed centrally.

For technical teams managing AI in production in Microsoft Fabric, the new integration enables a direct path from operating on raw data to building on knowledgeable artifacts that deliver accurate results at scale, without rebuilding data pipelines or managing separate retrieval infrastructure.

“The data enterprises need to power their AI agents already live in Microsoft OneLake,” said Ash Ashutosh, CEO, Pinecone. “Nexus builds task-specific artifacts from this data, and gives AI agents a clean, structured, cited interface through KnowQL, 30x+ faster and at a fraction of what traditional retrieval approaches cost.”

Today, every team building AI agents invents its own retrieval interface. The result is fragmentation, where agents retrieve information differently across platforms, governance is applied inconsistently, and cost controls break down at system boundaries. KnowQL defines a common language. An agent specifies the question, the required output structure, the citation standard, and the latency budget. A KnowQL-compliant knowledge engine handles the rest.

“Microsoft OneLake offers a unified data foundation for AI applications and Agents,” said Dipti Borkar, VP and GM, Microsoft OneLake and Fabric Ecosystem. “Pinecone Nexus does the hard work of fetching, assembling, and reasoning over OneLake data up front, so our customers’ agents spend less time making tool calls, burn fewer tokens, and get accurate answers faster.”

Nexus is built for enterprises with compliance requirements. Artifacts are assembled per task, scoped to RBAC and ABAC permissions, and versioned. Every answer cites its source. PII tagging and LLM processing rules are configured once and applied consistently. Token consumption is tracked across users and workloads through a unified dashboard.

Early access to Pinecone Nexus with OneLake integration is available now.