Databricks upgrades Genie agent with document analysis and sharing
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Databricks released an upgraded version of its AI agent Genie, which can complete long-running multi-step tasks without supervision. It draws the context it needs from information customers store on the Databricks platform, using an ontology built by Databricks. According to the Databricks blog, Genie Agents adds unstructured analysis of PDFs, documents, slides, and images in Unity Catalog Volumes, can answer questions across files and tables with page-level citations, and this feature is in public preview; native MCP connectors are now generally available; OpenSharing and Genie Clean Rooms are in testing. Genie respects Unity Catalog permissions, so users only see files they have access to; the creator chooses which tools each agent can use, and all connectors are governed through Unity Gateway. Databricks says costs can be controlled through Unity Gateway: enterprises can set usage limits and switch to lower-priced open-weight models.
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- TechCrunch · AIDatabricks’ new AI agent tells CEO Ali Ghodsi the unvarnished truth
Databricks unveiled an updated version of its AI agent Genie, which can now perform multi-step jobs unsupervised using an ontology built to give it context from customer data on the Databricks platform. CEO Ali Ghodsi demonstrated his own use of Genie to surface unvarnished truths he might not otherwise hear at a 12,000-employee company, citing Dunbar's number of 150 as the limit on direct relationships. Ghodsi said costs can be controlled through Databricks' Unity Gateway, which lets companies set usage limits and swap in less expensive open-weight models.
- Databricks BlogFeaturedFrom enterprise context to action: Genie Agents now analyze more data sources to transform your work
Databricks announced that Genie Agents can now analyze unstructured files in Unity Catalog volumes, connect to enterprise tools via generally available native MCP connectors, and be shared outside an organization through OpenSharing and Genie Clean Rooms (both in beta). The post cites customers including Unilever, HelloFresh, S&P Global Energy, Atlassian, General Motors and ModMed, and describes how agents parse documents, cite specific pages, and combine file content with governed tables.
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