The data-smart AI coworker, grounded in your enterprise context.
by Sydney Sundell, Ken Wong and Elise Georis
Despite the progress in LLMs and consumer chat agents, most enterprise teams still struggle to put AI to work on real business questions. The reason is that getting insights out of data - the ground truth of the business - is hard even with the latest generations of models and agents.
The reason for this is that the business context required to use data is scattered across dashboards, queries, pipelines, wikis, tickets, documents, and chat threads. When AI doesn’t easily find the information it needs, it fills in the gaps with inference, producing answers that are generic at best and wrong at worst. The current generation of agents often go through a process of iterative probing that is extremely slow and costly, and forcing a compromise in quality. This has resulted in unacceptably poor performance for truly data-driven decision making and actions.
Today, we are announcing our solution to this problem:

Genie began as a conversational analytics assistant in Databricks AI/BI. Genie One is the next step: a data-smart AI coworker designed to help users move from insight to action.

Because enterprise work happens across a full stack of tools and surfaces, we’re bringing Genie to everywhere work happens—starting by embedding it natively into Slack and Microsoft Teams. Users can simply @mention Genie in any conversation to ask questions in natural language and get accurate answers in seconds. Genie can also be used in public channels and threads, helping teams collaborate without switching context. Every response is governed, secure, and tailored to what each user is authorized to access.

For users on the go, we’re launching new iOS and Android apps that put Genie in your pocket. Users can ask questions, get alerts, and take action on insights grounded in your company data, from anywhere.
And for organizations who have adopted an existing AI agent or developed their own, we’re also announcing the Genie MCP App, which allows those users to benefit from Genie without having to change their workflows.

Our investment in building the Uplight Data Platform on top of Databricks is paying off in powerful new ways. By bringing Genie One capabilities to our data, we’re enabling teams across Uplight to explore, discover, and innovate with more speed, confidence, and creativity than ever before. This is the promise of data democratization - enabling a culture where curiosity, data-informed decision-making, and innovation can happen at every level of the company.—Micaela Christopher, Director of Data Science and Engineering, Uplight
Databricks customers have created more than a million Genie Spaces—curated, governed chat experiences scoped to specific topics, with verified logic and benchmarks. Now, Genie Spaces is evolving into Genie Agents: curated, domain-specific AI agents that:
Best of all, creating an agent is as simple as describing what you want: spin up a Genie Agent from a single prompt in Genie One or Genie Code, scope it, benchmark it, and share it for teammates to use or customize. Genie Agents let domain experts scale their expertise by turning trusted rules, data, and workflows into coworkers the whole team can rely on.

At Foot Locker, Genie Agents are transforming how we lead. They provide our executives and business teams with a centralized space to harness AI-driven insights across every North American banner we operate. As we scale Genie to the enterprise, it's reshaping the way our business interacts with data and makes the decisions that matter most. Genie isn't just a tool; it's the engine driving self-service insights across our organization—Matt Giunipero, VP of Data & Analytics and Krish Lakshminarayanan, VP, AI, Data & Analytics, Enterprise Architecture, Foot Locker
Genie One and Genie Agents are powered by Genie Ontology, an automatic context layer. Genie Ontology automatically extracts snippets of knowledge from tables, queries, dashboards, pipelines, and connected apps, and organizes that knowledge into a living graph of how a company works and what the data inside actually means. Genie has context about where to look, what to trust, and how to answer in a way that reflects how the company actually uses its data. That includes metric definitions, business terms, unique calculations, and the relationships between concepts, metrics, tables, and teams.

One key innovation of Genie Ontology is its approach to determining authority. Using an approach similar to PageRank, Genie Ontology weighs where a definition came from, the relative authority of that source’s author, how often people rely on it, how closely it ties to certified and widely-used assets, and how fresh it is. Then, Genie answers from the sources that carry the most weight. It also enforces the permissions of each source by only showing you the content that you actually have permissions to see. The result is that Genie solves the context problem, without asking your teams to hand-curate it or manage a separate permissions system.
Our internal benchmark of real-world enterprise data analysis tasks have shown that Genie Ontology significantly improves agent performance on complex, enterprise data questions. In our testing, Genie answered 84.5% of questions correctly on the first attempt, while the strongest general-purpose coding agent managed just 52.4% — and the weakest only 25%. And Genie doesn’t trade off accuracy for speed. Genie delivers high accuracy and low latency, 2× faster than the strongest coding agent.
To roll out any AI tool across a company, leaders and IT teams need confidence that it’s governed, secure, and ready to scale. That’s why Genie One includes a full suite of admin governance tools designed to help organizations deploy AI across their teams.
Like every Databricks product, governance and security sit at the heart of Genie. Permissions are enforced by default on every answer through source-native ACLs or Unity Catalog. MCP, tools and costs are governed by the Unity AI Gateway, providing a single pane of governance for admins.
Getting started
Genie One is the data-smart AI coworker every business user needs: it understands enterprise context, works across the tools where work happens, and is governed by design.
To try Genie One, see our documentation, install the mobile app for iOS or Android, or contact your Databricks account team.
Genie Spaces has evolved into Genie Agents, building on the curated, governed chat experiences that customers have already built more than a million of. These agents keep the verified logic and benchmarks that defined Genie Spaces, but add autonomous action such as scheduled tasks, document and artifact generation, and writes to external systems. They can also reason over unstructured data like documents and files, not just tables and views, giving them broader context than the original Genie Spaces.
The Genie MCP App lets organizations that have already adopted an existing AI agent, or built their own, tap into Genie's capabilities without changing their current workflows. It connects those agents to the same governed data and context that Genie One uses, so teams don't have to migrate away from tools they've already invested in. This makes Genie's insights accessible even to teams standardized on a different assistant platform.
Yes, Databricks has launched native Genie apps for iOS and Android so users can work with Genie from anywhere. The apps let people ask questions, receive alerts, and act on insights grounded in company data while on the go. Answers delivered through the mobile app are still governed by the same permissions enforced across Genie.
Genie has no seat-based pricing; organizations get up to $10 of free usage per user every month and pay only for the AI they actually use beyond that. Genie One, Genie Agents, and Genie Code are now generally available, while Genie App Builder and Genie ZeroOps are entering private preview shortly after the Data + AI Summit. This usage-based model means adoption isn't limited by license counts.
In Databricks' internal benchmark of 28 real-world enterprise data-analysis questions, Genie answered 84.5% correctly on the first attempt, compared with 52.4% for the strongest general-purpose coding agent tested and just 25% for the weakest. Genie reached this accuracy while running about 2x faster than the strongest competing coding agent. Databricks notes that the competing agents used in this benchmark were anonymized.
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