A Databricks Genie agent already gives your team a powerful, no-code way to ask questions of governed data in natural language. The question many teams reach next is how to make those answers richer and more consistent, especially when the context an agent needs lives across the whole estate and not only inside Databricks. This guide walks through the native context a Genie agent supports, then shows how Atlan’s context layer extends that context across every connected system and serves it back to Genie. The two work better together: Databricks brings the data and the horsepower, Atlan brings governed business meaning across your entire data and AI ecosystem.
Quick facts
Permalink to “Quick facts”| Attribute | Detail |
|---|---|
| What it is | A method for enriching a Databricks Genie agent with native context, then cross-estate context from Atlan |
| Status | Genie Agents are GA in Databricks; Genie Ontology announced June 16, 2026 at Data + AI Summit |
| Category | Agent context layer / context integration |
| Who it’s for | Data leaders, data architects, and AI teams running a Genie agent who want governed, cross-system context |
| Key benefit | Genie answers grounded in certified definitions that span every connected system, not only Databricks |
| Works with | Databricks Genie, Genie Ontology, Unity Catalog, plus Snowflake, dbt, Tableau, Power BI, Salesforce, SAP |
| How Atlan complements it | Atlan unifies context across the whole estate and serves it back to the Genie agent through its MCP server |
What a Databricks Genie agent is, and how native context works
Permalink to “What a Databricks Genie agent is, and how native context works”A Genie agent is a natural-language analytics surface in Databricks. A curator chooses the tables and views the agent can query, then adds context so Genie can interpret business questions and generate correct SQL against those datasets. Per the Databricks Genie Agent documentation, the curator’s job is to bridge the gap between Genie’s general world knowledge and the specialized language of a specific domain or company.
Databricks gives you several native ways to add that business context inside the agent:
- Table and column descriptions so Genie understands what each asset represents.
- Example SQL queries that teach Genie how to solve common questions in your domain.
- SQL expressions that define measures (KPIs), filters (conditions), and fields (attributes) as reusable business concepts.
- Plain-text instructions for guidance that applies across the whole agent.
- The Genie knowledge store, a curated set of semantic definitions that adds metadata customization, prompt matching, join relationships, and entity matching. Each agent supports up to 200 knowledge store snippets, while example queries and text instructions do not count toward that limit.
Alongside the agent itself, Databricks has been deepening platform-level context. Genie Ontology, announced June 16, 2026 at the Data + AI Summit, is described by Databricks as a self-improving context layer that learns your business from data and connected apps. Unity Catalog Metrics and the new Unity Catalog Business Glossary give agents governed, reusable definitions. These are excellent inside Databricks. The next section shows how to extend them across the rest of your estate.
See if your estate is ready to ground a Genie agent
Run a quick readiness check on the definitions, lineage, and ownership your agents will draw on across every connected system.
Assess Your ReadinessNative Genie context vs Genie plus the Atlan context layer
Permalink to “Native Genie context vs Genie plus the Atlan context layer”Native context makes a Genie agent accurate for the data and tools inside Databricks. Atlan adds the layer that spans everything else your enterprise runs, then serves it back to Genie. The frame is additive: each row below is something native context does well inside Databricks, with the cross-estate extension Atlan adds on top.
| Dimension | Native Genie agent context | Genie agent plus Atlan context layer |
|---|---|---|
| Scope of context | The data and tools connected inside Databricks | The whole estate: Databricks plus Snowflake, BigQuery, dbt, Tableau, Power BI, Salesforce, SAP, and 80+ connectors |
| Business definitions | Descriptions, SQL expressions, knowledge store snippets per agent | Certified glossary terms and ontology generated once and shared across every system |
| Lineage | Within Databricks assets | Column-level lineage reverse-engineered from SQL, across systems |
| How context is built | Curated by hand per agent | Context Agents auto-generate definitions; humans review edge cases |
| Trust and testing | Iterate on the agent through usage | Context Engineering Studio runs CI-integrated evals before context ships |
| Delivery to the agent | Read inside the Genie agent | Served to Genie and any agent through Atlan’s MCP server, SQL interface, and open APIs |
| Portability | Lives in the agent configuration | Iceberg-native, open formats; context stays portable, not locked to one schema |
This is the better-together model. Genie Ontology and Unity Catalog are strong grounding inside Databricks. Atlan unifies context across all the systems Genie alone does not reach, then delivers it back so the agent answers with full enterprise context. For the deeper architecture, see Genie Ontology and the Atlan context layer.
How to add enterprise context to a Databricks Genie agent: the steps
Permalink to “How to add enterprise context to a Databricks Genie agent: the steps”The path has five stages. The first builds the native context Databricks supports. The next four enrich the agent with governed, cross-estate context from Atlan and serve it back. The table summarizes the sequence, then each step is detailed below.
| Step | What you do | Where it happens | Outcome |
|---|---|---|---|
| 1 | Add native context to the Genie agent | Databricks | Genie understands your Databricks data |
| 2 | Connect Atlan across your estate | Enterprise Data Graph | A living graph of assets and relationships |
| 3 | Auto-generate definitions, terms, and ontology | Context Agents | Certified business context at scale |
| 4 | Certify and test the context | Context Engineering Studio | Context validated before production |
| 5 | Serve governed context to the Genie agent | Atlan MCP server and open APIs | Genie answers with full enterprise context |
Step 1: Add native context inside the Genie agent
Permalink to “Step 1: Add native context inside the Genie agent”Start where Databricks already helps you. In the Genie agent, add clear table and column descriptions, a small set of example SQL queries that reflect real questions, and SQL expressions for the measures and filters your team relies on. Add a focused set of plain-text instructions for guidance that applies agent-wide, and curate the knowledge store with prompt matching and join relationships. Databricks recommends iterating on the agent based on testing and usage, so treat this as a living configuration rather than a one-time setup.
Step 2: Connect Atlan to Databricks and your other systems
Permalink to “Step 2: Connect Atlan to Databricks and your other systems”Bring the rest of your estate into view. The Atlan Enterprise Data Graph uses 80+ connectors to pull assets and relationships from Databricks plus Snowflake, dbt, Tableau, Power BI, Salesforce, SAP, and more into a single living graph, with column-level lineage reverse-engineered from SQL. This is the foundation that lets context span systems a single Genie agent does not reach on its own. For the connection details, see the context layer for Databricks guide.
Step 3: Auto-generate definitions, glossary terms, and ontology with Context Agents
Permalink to “Step 3: Auto-generate definitions, glossary terms, and ontology with Context Agents”Now generate the business meaning at scale. Context Agents are AI teammates that auto-generate descriptions, link glossary terms, infer metrics, and propose ontology relationships from the Enterprise Data Graph. Per Atlan AI Labs (April 2026), Context Agents have generated 690K+ descriptions, with 87% rated on par or better than human writing across 50+ enterprise customers. These become the certified definitions your Genie agent can stand on.
Inside Atlan AI Labs and the 5x accuracy factor
See how context engineering drove a 5x accuracy improvement in real customer systems, with experiments, results, and a repeatable playbook.
Download the EbookStep 4: Certify and test context in Context Engineering Studio
Permalink to “Step 4: Certify and test context in Context Engineering Studio”Generated context still needs to be trustworthy before agents use it. Context Engineering Studio lets you bootstrap, test, and ship context as code, with CI-integrated evals that validate definitions before they reach production. Humans review the edge cases, and a versioned context repository tracks every change. This is the step that answers the common question of how to verify and trust agent-generated context: it is certified and eval-tested, not assumed.
Step 5: Serve governed context to the Genie agent and keep it current
Permalink to “Step 5: Serve governed context to the Genie agent and keep it current”Finally, deliver the context to Genie. Atlan stores context in the Context Lakehouse, an Iceberg-native open-format store, and activates it through its MCP server, SQL interface, and open APIs. A Genie agent reads governed definitions, lineage, ownership, and policy context from that single source of truth at query time. Because Context Agents re-scan connected systems and Context Engineering Studio re-runs evals on changes, the context the Genie agent draws on stays current. That continuous loop is how context compounds rather than decays. For the broader pattern, see how to implement an enterprise context layer for AI.
Why cross-estate context is the difference for a Genie agent
Permalink to “Why cross-estate context is the difference for a Genie agent”A Genie agent answers best when the context behind it is governed, consistent, and complete. Native context makes it accurate for the data inside Databricks. The accuracy ceiling, though, is set by how much of the business the context actually covers. Most enterprise estates span many systems, and a question about customer churn often needs definitions from Salesforce, transformation logic from dbt, and metric definitions from Tableau or Power BI, not only Databricks schemas.
That is the positive case for adding Atlan: it extends the meaning a Genie agent can reach to the entire estate, then serves it back through a single governed source of truth. Atlan AI Labs measured a 5x accuracy improvement in agents grounded in Atlan’s context layer, and notes that 83% of AI pilots never reach production because the gap is context, not the model. Genie One and Genie Ontology give Databricks teams a strong start; Atlan makes that start span every system the business runs. Better together is not a slogan here, it is the architecture: Databricks brings the data and the agents, Atlan brings governed context across the whole ecosystem.
Watch the Atlan context layer live
See how teams build, certify, and serve governed context to agents like Genie across the whole estate.
Watch Context Layer LiveContext that spans the estate is what makes a Genie agent production-ready
Permalink to “Context that spans the estate is what makes a Genie agent production-ready”Adding context to a Databricks Genie agent is two complementary moves. First, use the native context Databricks supports: descriptions, example SQL, SQL expressions, instructions, and the knowledge store, and iterate on them through real usage. Second, extend that context across your whole estate with Atlan.
The stack that makes a Genie agent production-ready:
- Native Genie context grounds the agent in your Databricks data
- Atlan’s Enterprise Data Graph connects Databricks plus 80+ other systems into one living graph
- Context Agents auto-generate the certified definitions and ontology Genie depends on
- Context Engineering Studio tests and certifies that context before it ships
- Atlan’s MCP server and open APIs serve the governed context back to the Genie agent, and keep it current
A Genie agent plus Atlan equals agents with full enterprise context, not only Databricks context. The two are additive. The question for your team is not whether to use a Genie agent, it is how much of your business the context behind it can reach.
FAQs about adding context to a Databricks Genie agent
Permalink to “FAQs about adding context to a Databricks Genie agent”- What is a Databricks Genie agent?
A Genie agent is a no-code, natural-language analytics surface in Databricks. A curator picks the tables and views the agent can use, then adds context (descriptions, example SQL, SQL expressions, and instructions) so Genie can translate business questions into accurate SQL against those datasets.
- What native context can you add to a Genie agent?
Databricks supports several native context types: table and column descriptions, example SQL queries, SQL expressions that define measures and filters, plain-text instructions, and the Genie knowledge store with metadata customization, prompt matching, and join relationships. Each agent supports up to 200 knowledge store snippets, with example queries and text instructions not counting toward that limit. (Source: Databricks Genie knowledge store docs, 2026)
- How does Atlan add context to a Databricks Genie agent?
Atlan connects to Databricks and your other systems through the Enterprise Data Graph, auto-generates certified definitions and glossary terms with Context Agents, tests them in Context Engineering Studio, then serves that governed context back to the Genie agent through Atlan’s MCP server and open APIs. The result is context that spans the whole estate, not only Databricks.
- Does Atlan replace Genie Ontology or Unity Catalog?
No. Genie Ontology and Unity Catalog are excellent inside Databricks. Atlan extends and unifies context across the entire data and AI ecosystem, including Snowflake, dbt, Tableau, Power BI, Salesforce, and SAP, then serves it back to your Genie agent. The two are additive, better together, never competing.
- How does context reach a Genie agent from Atlan?
Atlan stores context in the Context Lakehouse, an Iceberg-native, open-format store, and activates it through its MCP server, SQL interface, and open APIs. A Genie agent reads governed definitions, lineage, ownership, and policy context from that single source of truth at the moment an agent needs them.
- How do you keep Genie agent context current?
Context compounds when it is maintained continuously. Atlan’s Context Agents re-scan connected systems and refresh definitions as the estate changes, Context Engineering Studio re-runs evals before any update reaches production, and the refreshed context flows back to the Genie agent through MCP automatically.
- Why add cross-estate context when Genie already has native context?
Native Genie context is bounded to the data and tools inside Databricks. Most enterprises also run Snowflake, BigQuery, dbt, Tableau, Power BI, Salesforce, and SAP. A question like customer churn often needs definitions from those systems. Atlan unifies context across all of them and serves it back, so the Genie agent answers with full enterprise context.
Sources
Permalink to “Sources”- Set up a Genie Agent, Databricks Documentation
- Tune Genie Agent quality, Databricks Documentation
- Curate an effective Genie Agent, Databricks Documentation
- Connect Genie Code to MCP servers, Databricks Documentation
- Databricks Launches Genie One: All-New Agentic Coworker for Every Team, Databricks Newsroom
- What’s new with Unity Catalog at Data + AI Summit 2026, Databricks Blog
- Not pagerank, but ontorank: Databricks Genie Ontology brings context and authority to AI, ITdaily
- Everything Databricks Announced at the DAIS Data + AI Summit 2026, Qubika
- Key takeaways from day two of Databricks Data + AI Summit, SiliconANGLE
