Building the AI-Native Enterprise, Together
See how Databricks Genie, grounded in Atlan's context layer, powers AI agents that reason from certified data, enforce governance, and explain every output.
Trusted by 500+ data-forward enterprises
About this Session
At enterprise scale, AI agents multiply faster than shared context does, leaving every agent reasoning from a different version of the truth. The result: conflicting answers, eroding trust, and AI that gets harder to govern as it scales.
Databricks and Atlan solve this together. Databricks Genie provides the intelligence layer: querying the data estate, reasoning over it, acting on what it finds. Atlan provides the context Genie needs: semantics, quality signals, and governance policies that make those answers trustworthy and explainable.
Join the Databricks and Atlan teams on May 20 to see how to build that context layer from the ground up.
What you'll see
01
Genie without context vs. with context
Same business question answered twice: first by a Genie Space reasoning over raw tables and columns, then by the same Genie logic inside an Agent Brick with Atlan's MCP server and context repositories wired in. Same intelligence, see what context changes about the answer.
02
Engineering Genie-ready context
See how Atlan's Context Engineering Studio turns raw Databricks assets into metric views, Genie Space configurations, and semantic relationships, all generated from the enterprise data graph instead of hand-written prompt hacks.
03
Governance and quality in Genie's decision loop
See how Atlan's policies and Data Quality Studio checks flow into Genie as constraints and signals, so agents can prefer trusted data products, respect access rules, and explain where every number came from.
04
From POC to production grade agents
Learn a concrete rollout pattern for Databricks + Atlan: from wiring connectors to deploying governed Agent Bricks, so platform teams can move from POC to production-grade agents faster, without trading off safety for speed.
Discover more from Atlan in Action
Most teams have data. What they're missing is the layer that makes AI trustworthy. In this 45-minute live session, you'll see Atlan's context layer in action — from enriching raw data assets to engineering a semantic model, running evals, and watching an AI analyst answer business questions correctly.
Dataplex gives you strong native cataloging across your GCP estate, and Atlan adds the context layer that spans across your entire enterprise. Join us to see how the Dataplex and Atlan work together to make your data AI-ready.
What Our Users Say
“We built a revenue analysis agent and it couldn't answer one question. We started to realize we were missing this translation layer. We had no way to interpret human language against the structure of the data.”
— Joe DosSantos, VP, Enterprise Data & Analytics, Workday
“Within the first year after that we cataloged over 18 million assets, defined more than 1300 glossary terms. Atlan had lineage across our on-prem Oracle databases, BigQuery, and Looker.”
— Kiran Panja, Managing Director, Cloud & Data Engineering, CME Group
“We're scaling context development as much as possible, and where can we leverage Atlan AI to build the most robust definitions across our data estate.”
— Takashi Ueki, Head of Enterprise Data & Analytics, Elastic
“Atlan gives us a UI that our community can use to edit, update and manage classifications as well as other metadata enrichments into a verified state.”
— Sherri Adame, Enterprise Data Governance Leader, General Motors
“All of the work that we did to get to a shared language amongst people at Workday can be leveraged by AI via Atlan's MCP server.”
— Joe DosSantos, VP, Enterprise Data & Analytics, Workday
“Atlan's metadata lakehouse is configurable across all our tool sets and is flexible enough to get us to a future state.”
— Andrew Reiskind, CDO, Mastercard
“Atlan is much more than a catalog of catalogs. It's more of a context operating system… Atlan enabled us to easily activate metadata for everything from discovery in the marketplace to AI governance to data quality to an MCP server delivering context to AI models.”
— Sridher Arumugham, CDAO, DigiKey
""We built a revenue analysis agent and it couldn't answer one question. We started to realize we were missing this translation layer. We had no way to interpret human language against the structure of the data.""
— Joe DosSantos, VP, Enterprise Data & Analytics, Workday
""Within the first year after that we cataloged over 18 million assets, defined more than 1300 glossary terms. Atlan had lineage across our on-prem Oracle databases, BigQuery, and Looker.""
— Kiran Panja, Managing Director, Cloud & Data Engineering, CME Group
""We're scaling context development as much as possible, and where can we leverage Atlan AI to build the most robust definitions across our data estate.""
— Takashi Ueki, Head of Enterprise Data & Analytics, Elastic
""Atlan gives us a UI that our community can use to edit, update and manage classifications as well as other metadata enrichments into a verified state.""
— Sherri Adame, Enterprise Data Governance Leader, General Motors
""All of the work that we did to get to a shared language amongst people at Workday can be leveraged by AI via Atlan's MCP server.""
— Joe DosSantos, VP, Enterprise Data & Analytics, Workday
""Atlan's metadata lakehouse is configurable across all our tool sets and is flexible enough to get us to a future state.""
— Andrew Reiskind, CDO, Mastercard
""Atlan is much more than a catalog of catalogs. It's more of a context operating system… Atlan enabled us to easily activate metadata for everything from discovery in the marketplace to AI governance to data quality to an MCP server delivering context to AI models.""
— Sridher Arumugham, CDAO, DigiKey