Shell modernized global energy analytics with a high-performance lakehouse powered by Dremio.
AI Use Cases
Give your AI agents data they can actually use
Dremio connects any agent to governed, federated enterprise data in minutes, with no pipelines, no lock-in, and no manual tuning.
Trusted by leading COMPANIES
BENEFITS
Built for teams running AI at enterprise scale
Dremio gives AI agents the connectivity, context, and performance it needs to deliver trusted answers across your entire data estate, without pipelines, proprietary lock-in, or manual tuning.
Connect any agent in minutes
Explore our AI Agent →
One source of truth across every system
Dive into the AI Semantic Layer →
Governed, fast answers at any scale
Learn about Dremio's intelligent query engine →
HOW IT WORKS
Three steps to your first agentic answer
Point any MCP-compatible agent at Dremio’s hosted MCP server using a Personal Access Token. Claude Desktop, ChatGPT, Cursor, Windsurf, and coding agents like Claude Code all connect in minutes with no installation required on Dremio Cloud.
Type your question in plain language and your agent translates it into governed SQL across your entire data estate. Dremio federates the query across 31+ connected sources simultaneously, with no data movement and no manual joins required.
Every result comes back with the same row-level permissions, column masking, and business logic that govern your human analysts. Autonomous Reflections automatically accelerate repeated queries over time, so the more your agents work, the faster and cheaper they get.
CUSTOMER STORIES
Trusted by data teams worldwide
Genomics England uses Dremio to provide 1,800+ researchers self-service access to genomic data across 280,000 participants.
World Bank Group transformed capital markets operations with an agentic lakehouse built on Dremio.
FAQs
Common questions
What analysts and data leaders ask us most about agentic analytics.
Dremio connects to any MCP-compatible agent or LLM through its open MCP server, including Claude Desktop, ChatGPT, Cursor, and Windsurf. The Dremio CLI provides direct integration for coding agents like Claude Code and Codex. Custom agent frameworks built on LangChain or LlamaIndex connect via REST API or Apache Arrow Flight. Because Dremio uses the open Model Context Protocol rather than proprietary integrations, you are not locked into any specific AI provider or framework.
The AI Semantic Layer is Dremio’s unified business context layer spanning all connected data sources. It stores metric definitions, dimension labels, calculated fields, and AI-generated wiki descriptions shared by every agent and analyst querying through Dremio. Without a semantic layer, agents produce inconsistent or incorrect answers because the same field means different things in different source systems. Dremio’s AI Semantic Layer is unique in that it spans the entire federated data estate, unlike Databricks metric views or Snowflake semantic views, which are scoped to a single platform only.
Governance for agents works identically to governance for human users. Every query, regardless of whether it originates from a person or an AI agent, passes through Dremio’s unified access control layer: role-based access control with SQL GRANT/REVOKE syntax, row-level filtering, and column masking enforced at the Apache Polaris catalog layer. Agents authenticate via Personal Access Token and inherit the same permissions as the identity they represent. Dataset lineage traces every data access across all connected sources, providing a complete audit trail for compliance review.
Autonomous Reflections are Dremio’s automatic query acceleration system, available in Dremio Cloud. Dremio analyzes query patterns over a rolling 7-day window and automatically creates, refreshes, and retires materialized accelerators. No manual configuration or tuning required. AI agents benefit directly: repeated analytical queries are served from pre-computed Iceberg tables, delivering sub-second results. For organizations running high-volume agent workloads, Autonomous Reflections can reduce compute consumption by 30-70% compared to running every query cold against source systems.
For Dremio Cloud customers, connecting an agent takes minutes. The hosted MCP server requires no installation: generate a Personal Access Token, point your agent’s MCP client at the hosted endpoint, and the agent can immediately start discovering datasets and running governed queries. For self-hosted deployments, the dremio-mcp Python package installs in a single command and registers as a standard MCP server in any MCP-compatible client. Full setup documentation is at docs.dremio.com/current/developer/mcp-server/.
DISCOVER RESOURCES
Expand your knowledge
From Bottlenecks to Breakthroughs: A Hands-on Intro to Agentic Analytics for the Data Analyst
By unifying federation, a robust semantic layer, and autonomous performance tuning, Dremio transforms the lakehouse from a static repository into an active partner.
Dremio Architecture Guide
Dremio’s Architecture Guide explains how its lakehouse delivers scalable, cost-efficient, self-service analytics by eliminating ETL, enabling instant data access, and accelerating queries.
Data Meaning: Why the Semantic Layer Is the Brain of Agentic Analytics
The investment in the semantic layer pays off not just in agent accuracy but in the reliability of every downstream workflow that depends on agent output.
