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AI capabilities

Databricks provides a comprehensive AI platform for building, deploying, and integrating intelligent applications. This page covers agent frameworks, AI functions, vector search, and integration patterns for partners.

For governance principles, see Data and AI governance for the data lakehouse.

AI agent integrations​

AI agents enable intelligent, context-aware workflows for partner integrations. Partners can build code-based agents or use Databricks AI/BI Genie. Databricks provides a unified, governed foundation for building, running, and integrating agents without maintaining external infrastructure.

AI Agent Framework and MLflow​

Partners can build agents using open-source frameworks like LangChain, LangGraph, LlamaIndex, DSPy, CrewAI or other. The AI Agent Framework provides structured tool invocation, SQL execution, vector search integration, and multi-step reasoning within a Python development model.

MLflow provides the production lifecycle—experiment tracking, packaging, version control, and deployment. Agents run directly on Databricks with low-latency access to governed data, models, vector stores, and tools. Unity Catalog enforces governance across all agent resources.

Documentation: Author AI agents in code | MLflow 3 for GenAI | Deploy models using Mosaic AI Model Serving

Natural language analytics​

Databricks Genie provides a governed natural-language interface for querying Unity Catalog data. Partners configure a Genie Space with curated tables, metadata, and example queries. Genie translates natural-language questions into executable SQL and returns results.

Agents can call Genie through APIs or MCP to handle natural-language data access and analytical reasoning. This enables partner applications to embed conversational analytics and self-service data experiences with built-in governance.

Documentation: Use Genie in multi-agent systems | Use the Genie API to integrate Genie into your applications

External agents​

Partners with agents running outside Databricks can integrate via MCP or foundation model APIs.

MCP​

Through MCP, external agents can securely delegate tasks to Databricks—executing SQL, retrieving insights via Genie, applying AI Functions, or invoking tools. This supports hybrid architectures where partner agents run externally but rely on Databricks for secure data access and task execution.

Documentation: MCP on Databricks | Unity Catalog Tools

MCP Marketplace​

Partners can publish their own MCP servers to the Databricks Marketplace, making partner tools discoverable and installable by joint customers. Published MCP servers integrate directly with Databricks-hosted agents, enabling tool invocation through a standardized interface.

Documentation: MCP Marketplace Validation

Foundation models​

External agents can access open-source and proprietary models (OpenAI, Anthropic, Google) through a unified serving layer with REST and OpenAI-compatible APIs. No separate infrastructure or credentials needed per provider.

Documentation: Foundation Models | Foundation Model APIs

Databricks-hosted agents and LLMs​

Partners can invoke Databricks-hosted agents, LLMs, and custom models through several fully supported interfaces that provide flexibility to integrate:

  • AI Functions, such as ai_query, callable from SQL or Python to access foundation models, Databricks-hosted models, agents, or external models through a single governed interface. They are also optimized to support batch inference use cases.
  • REST APIs, which allow any external application or service to send inference requests to Databricks-deployed models or agents.
  • OpenAI-compatible APIs, enabling partners to use standard OpenAI client libraries and request formats to call Databricks models and agents with minimal code changes.

Documentation: Model Serving | AI Functions

AI functions​

AI Functions are built-in SQL functions for applying AI to data stored on Databricks. They run from Databricks SQL, notebooks, Lakeflow SDP, and Workflows.

  • ai_query - General-purpose function to call any AI model (Foundation Model APIs, external models, custom ML models) through a single SQL interface
  • Task-specific functions - High-level capabilities like summarization and translation, powered by Databricks-managed models

Use cases​

  • BI tools - Summarize dashboards, explain anomalies, generate natural-language insights
  • ETL pipelines - Extract entities, summarize documents, generate structured fields from unstructured data
  • SQL-based tools - Add text generation or reasoning capabilities where the data lives

Documentation: AI Functions | ai_query | Task-specific Functions

Databricks Vector Search is a serverless, production-grade vector database built into the platform. Create and query vector search indexes backed by Delta tables, governed by Unity Catalog.

For ingest partners​

Compute embeddings and write them to Delta tables, then create indexes using:

  • Delta Sync Indexes - Automatically sync vectors from a source Delta table on updates
  • Direct Vector Access (DVA) - Upsert embeddings directly via the Vector Search API

Use your own embedding models or Databricks-hosted models. All indexes are registered in Unity Catalog.

For agent partners​

Query indexes via REST API or SDK to retrieve relevant vector matches:

  • similarity_search - Nearest neighbors by vector similarity
  • query with metadata filters - Match embeddings and structured filters

Documentation: Vector Search | Create and Query Indexes | Agent Bricks

What's next​