Databricks Genie Agents 新增非结构化数据分析与 MCP 连接器

Thousands of teams use Genie Agents to turn deep operational questions into actionable outcomes every day. Across industries, organizations are putting Genie Agents to work on some of their most important business challenges:

• Retail and consumer goods: Unilever uses Genie Agents to help finance teams explore profitability, credit risk and financial performance in natural language. HelloFresh uses Genie Agents to increase ad-testing velocity by 4x by unifying marketing performance insights across media, creative and finance teams.
• Energy: S&P Global Energy uses Genie Agents to bring new data products to market in days, rather than through traditional development cycles. Its domain experts publish governed, conversational access to commodity data themselves, from LNG cargoes to chemical capacity. Customers can query that data directly from their own AI agents.
• Technology: Atlassian uses Genie Agents to help security teams investigate billions of security events through natural language, accelerating threat hunting and incident response.
• Automotive: General Motors has deployed over 1000 Genie agents across sales teams, automotive engineers, supply chain specialists, HR operations, and more, all grounded in the specific context of each line of business.
• Healthcare and life sciences: ModMed uses Genie Agents to give nontechnical teams secure, natural language access to enterprise data, using catalog structure, permissions and business context to answer questions without requiring a custom AI application for every request.

What these use cases have in common is the need for agents that deeply understand each business's unique context. Genie Agents ground their responses and actions in rich knowledge curated within Databricks, helping teams move from natural-language questions to trusted insights and action.

As demand for context-rich workflows grows, agents need to reason beyond structured business data alone. They need to understand context from unstructured assets and connect to enterprise tools so they can reason across the full business environment and take action in the tools that run their business.

What’s new with Genie Agents

Genie Agents started as the fastest way to turn business questions into SQL insights. Today, they have evolved into something bigger: a no-code agent that understands your entire business context. With unstructured data analysis and new MCP connectors, Genie Agents can conduct high-quality business analytics, analyze large volumes of documents and connect to your enterprise tools to take action on your behalf.

And now with support for OpenSharing, Genie Agents can accelerate time-to-insights outside your enterprise. You can share a governed agent that's pre-loaded with your data, instructions and knowledge with partners, suppliers and customers. External teams can immediately use Genie Agents to get value out of your data, without spending time cross-collaborating on insights.

Genie Clean Rooms go further, offering privacy-centric agentic collaboration. Your recipients get a Genie Agent to chat with your data. But the underlying dataset and IP are hidden and fully protected. These updates let you extend governed Genie Agents to every stakeholder in your ecosystem.

Unstructured context requires more than basic enterprise search

80% of the context that runs an enterprise lives in documents: contracts, invoices, claims, medical records, financial reports and call transcripts. Unlocking this context at scale can transform your business.

Most agents today rely on basic enterprise search via MCP connectors, which let you attach a handful of documents directly and pull out the raw text as context in the session. However, many enterprise use cases involve large document corpora or complex file structures. Maintaining context and compliance here requires understanding the document structure and its relationships while creating a secure perimeter for governing enterprise data.

Building these workflows at scale also requires developing custom RAG, including advanced parsing capabilities, building a separate AI Search endpoint, writing an embedding pipeline, summarizing document insights and passing those summaries to a supervisor agent framework with robust permissions.

A bespoke approach to large-scale unstructured analysis can therefore quickly become expensive to build and maintain.

Genie Agents simplify unstructured data analysis

Genie Agents simplify unstructured data work for your team by automatically handling document parsing, indexing and context retrieval to return high-quality results along with specific citations.

Genie Agents use our custom AI document parsing function to read each file the way a person would. They recognize tables, figures, headers and footers and preserve the surrounding context. Because parsing and indexing run at scale, one agent can work through thousands of files, searching for the ones relevant to your question and citing the exact page for each answer.

What’s powerful about doing this in Genie Agents is that users can now ask questions that span files and tables. “Customers like Verana Health leverage Databricks today to process and analyze real-world clinical data at scale, generating meaningful insights from their healthcare datasets using Genie Agents.

“Our team is now looking to expand these capabilities with Genie Agents to unlock insights from unstructured clinical data and other clinical documents. We're excited about the potential to make this information more accessible through natural language, enabling our teams to explore richer clinical insights alongside structured data.”- Vinodh Simon, Director of Engineering, Verana Health

Let's look at a few additional examples.

Combining quantitative and qualitative insights across tables and files.

An investment analyst sees that a portfolio company's margin has declined for three consecutive quarters. That evidence might exist in analyst reports, earnings call transcripts and industry research PDFs. A Genie Agent pulls margin and selling-price trends from governed tables to find which product lines account for the decline. It then searches the attached analyst reports and earnings transcripts for what competitors did over the same quarters. Genie provides analysts with data trends, the likely causes and page-level citations for each claim.

Answering questions with files as cited references.

In many businesses, users need to complete a task and need guidance from internal documents. The fix might be in one of thousands of service manuals, bulletins and troubleshooting guides, which cover different models and revisions. In this example, the agent identifies the model, searches only the relevant manuals and service bulletins out of thousands of documents and returns the troubleshooting steps with a citation to the source page. AI parsing keeps tables, diagrams and numbered steps intact, so the technician gets the whole procedure.

Analyze data patterns and trends across files.

Sometimes users need qualitative analysis across multiple unstructured files. A clinical trial analyst notices in the trial-tracking data that one department is running fewer studies. The reasons may be written in investigator and doctor reports, spread across dozens of PDFs. The agent confirms the trend in the trial data, then reads that department's doctor reports and pulls out the reasons that keep coming up. Each one is cited back to the source, so the analyst can check the evidence before acting. Reading those reports one by one might take days.

How unstructured search works inside Genie Agents.

Agent builders can attach Unity Catalog Volumes containing PDFs, documents, slide decks or images directly to a Genie Agent. The agent retrieves the file content most relevant to the question and adds it to its context. The agent then reasons over that content together with any structured data to generate an answer with citations. These citations can point to the specific page of the source file. Users can ask questions across a specific volume, multiple volumes and structured data in the same conversation.

Unstructured data analysis runs in Agent mode, using multi-step reasoning to retrieve and analyze the most relevant file content. It respects Unity Catalog permissions, meaning users only see files they have access to, and supports content search indexing for faster, higher-quality retrieval across large volumes of data.

Lakeflow Connect and MCP connectors expand your agent reach

Genie Agents now give you more ways to connect business context across your systems and tools 

Bring structured data in with Lakeflow Connect. Lakeflow Connect lets you ingest data from apps like Salesforce, Workday, SAP or Marketo into Unity Catalog. Once in the Lakehouse, Genie Agents analyze it as accurately as any other governed table, using your curated metric views

Take action with MCP connectors. Native MCP connectors, now GA, let agents get context from tools like Slack, Google Drive, Google Calendar, Gmail, Glean, Atlassian, Github and Microsoft 365. Genie Agents can also act by creating tickets, updating records and posting messages. Authors can choose exactly which tools each agent can use, and every connector is governed through Unity Gateway.

What this unlocks:

Structured data, documents and tools sit under the same governance, so one agent can reason across all three and take action on those insights

Take a sales team getting ready for a quarterly pipeline review. A Genie agent analyzes pipeline data in Databricks, pulls in account plans from Google Drive and win-loss reports from UC volumes, then posts a deal strategy summary to the team's Slack channel. Agent authors can limit the Slack connector to posting summaries only, and admins govern every connection through Unity Gateway. That turns hours of manual prep into an automated workflow that keeps the whole team aligned.

“Teams make their best decisions when trusted context shows up in the flow of work. With Genie Agents now connected to Slack, sales, finance and operations teams can go from question to action without leaving the conversation. We’re thrilled to work alongside Databricks to bring governed, reliable insights to where the work happens.”- Jaime DeLanghe, Chief Product Officer, Slack

Genie Agents can now enable cross-organization collaboration and intelligence monetization

With OpenSharing for Genie Agents, a provider can now share a fully functional Genie Agent with an external recipient, pre-loaded with instructions and context, across Databricks accounts. Under the hood, OpenSharing provides live access to the underlying data. This opens up use cases for data monetization, partner collaboration and cross-org analytics without giving up control of your data infrastructure. Customers or data partners can sell "data intelligence” through packaged agents in the Databricks Marketplace alongside data providers such as FactSet or LSEG and monetize this with their customers.

For sharing use cases that require privacy protection, such as sensitive healthcare or financial data, Genie Clean Rooms let you share an agent where the recipient can ask questions and get answers, but cannot see the underlying data or your IP. This opens up exciting new possibilities for data sharing, where data previously too sensitive to share in its entirety may now be sanitized and shared. Genie Clean Room also offers additional controls, such as conversation limits and output size controls, so you stay in control of your data and how recipients use it.

Accuracy and reliability

Genie Agents are accurate because they understand your business, not just your schema. Every agent is grounded in the enterprise context layer that learns your business definitions, metrics, table relationships and column meanings. Subject matter experts then add to that context in three ways:

• Metric views define each business metric once in Unity Catalog. The agent reads its measures, dimensions, descriptions and synonyms directly.
• SQL expressions and example queries set joins, measures and filters. When someone asks about "active customers," the agent uses your definition and joins the right tables on the first try.
• Instructions cover terminology and rules specific to your organization, such as how sales tells committed accounts apart from prospects.

Benchmarks make Agents more reliable over time. They work like unit tests: you define your most common questions with gold-standard SQL answers, score the agent and rerun after changes to catch regressions. Genie suggests fixes such as missing joins, filters or descriptions. In production, the monitoring tab and user feedback show where answers fall short, so the agent improves with use.

Moving from answers to autonomous work

The next wave of agents won't wait for someone to open a chat. They'll monitor pipelines, triage escalations, review at-risk deals and answer partners’ questions around the clock. An agent working on its own needs more context, your metrics, your terminology and the guardrails to do its job.

Genie Agents are built for this work. They can be scoped for a business domain or workflow with its own data, files, tools and instructions. Teams can define this agent once, then deploy it through APIs, custom applications and automated workflows. Genie One and Genie MCP can also call these agents when a task needs deeper investigation.

Each agent you build also adds context that the rest of the company can use. A metric defined for finance is available when sales builds its agent, and a correction from one team can become a governed definition for everyone. You don't have to finish mapping the business before you put agents to work, because building context and deploying agents feed each other.

This week's launches expand all three parts of that work. Genie Agents can now reason over thousands of documents alongside governed tables, take action in the tools your teams already use and serve partners and customers outside your company, with your governance applied throughout.

For more information, review these resources:

• How to choose your first Genie Agents for maximum impact — a scoring framework for picking the right first use case
• Best Practice for Building Genie Agents — best practices from data prep to production
• Genie Agents documentation — setup guides, API reference, and configuration details

FAQs

What’s new in Databricks Genie Agents?

Genie Agents can now analyze unstructured files in Unity Catalog volumes, connect to enterprise tools and be shared outside your company. Unstructured data analysis is in Public Preview, native MCP connectors are generally available, and OpenSharing and Genie Clean Rooms for Genie Agents are in beta.

How do Genie Agents analyze unstructured documents?

Agent builders attach Unity Catalog volumes that contain PDFs, documents, slide decks or images to a Genie Agent. The agent retrieves the most relevant file content, reasons over it together with structured data and answers with citations that can point to the specific page of the source file. It respects Unity Catalog permissions, so users only see files they have access to.

Which enterprise tools can Genie Agents connect to?

Lakeflow Connect ingests data from apps like Salesforce, Workday, SAP or Marketo into Unity Catalog, where Genie Agents analyze it like any other governed table. Native MCP connectors let agents get context from tools like Jira, Confluence, Slack, Google Drive and Microsoft 365, and act by creating tickets, updating records and posting messages. Authors choose which tools each agent can use, and every connector is governed through Unity Gateway.

Can I share a Genie Agent with partners or customers outside my company?

Yes. With OpenSharing, a provider can share a fully functional Genie Agent, pre-loaded with instructions and context, with an external recipient across Databricks accounts. For sensitive data, Genie Clean Rooms let recipients ask questions and get answers without seeing the underlying data or your IP, with controls such as conversation limits and output size limits.

How do Genie Agents stay accurate?

Genie Agents are grounded in an enterprise context layer that learns your business definitions, metrics, table relationships and column meanings. Subject matter experts add metric views, SQL expressions and example queries, and instructions. Benchmarks work like unit tests to catch regressions, and the monitoring tab and user feedback show where answers fall short.