Table of Contents
Quick answer
Databricks offers powerful AI capabilities through its comprehensive suite of features designed for enterprise data teams. The platform combines Agent Bricks for automated AI agent creation, AI/BI Genie for natural language querying, and the Mosaic AI framework for scalable model deployment and serving.
Recent additions include GPT OSS model hosting, enhanced Power BI integration with M2M OAuth, and serverless Postgres layers through Lakebase. These tools work together to eliminate the complexity between data preparation and AI deployment, making advanced analytics accessible to business users while maintaining enterprise-grade security and governance.
Does Databricks have an AI agent?
Yes. Databricks revolutionizes AI agent creation with Agent Bricks.
This isn’t just another chatbot builder. Agent Bricks automatically constructs high-performing, domain-specific AI agents tailored for your business needs. You specify the task. Connect your enterprise data. Agent Bricks handles everything else.
The magic happens behind the scenes. Mosaic AI Research generates synthetic benchmarks that systematically tune agent quality and cost according to your unique business data. No more manual trial-and-error. No more guessing what works.
These agents excel at information extraction, knowledge assistance, custom text transformation, and orchestrated multi-agent systems. Every interaction gets logged and governed through Unity Catalog, ensuring your data stays secure and compliant.
Ready to build? Check out the Databricks Agent Framework documentation to get started.
What are the latest Databricks AI features?
1. Agent Bricks (June 2025)
Agent Bricks transforms how organizations deploy AI agents. This breakthrough feature automates the entire lifecycle—creation, optimization, and evaluation—of enterprise AI agents within Databricks.
The secret sauce? Mosaic AI Research generates synthetic benchmarks that systematically tune agent quality and cost according to your unique business data. No more manual testing. No more expensive trial-and-error deployments.
“Agent Bricks automatically optimizes AI agents on customers’ unique data to deliver cost-efficient, trustworthy agents. Just provide a high-level description of the agent’s task, and connect your enterprise data—Agent Bricks handles the rest.” – Databricks, June 11, 2025
Key capabilities:
- Automated agent architecture selection
- Synthetic benchmark generation for quality tuning
- Cost optimization based on business requirements
- Seamless transition from prototype to production
2. Databricks-hosted Foundation Models in Mosaic AI Model Serving (August 2025)
Mosaic AI Model Serving now supports OpenAI’s GPT OSS 120B and GPT OSS 20B models, hosted natively within Databricks infrastructure. This eliminates the complexity of external API management while providing enterprise-grade security.
Teams can run batch inference workloads and invoke these large models via Foundation Model APIs. The pay-per-token pricing model makes it easy to integrate advanced generative AI capabilities without upfront commitments.
Key benefits:
- Native hosting within Databricks security perimeter
- Simplified API access for enterprise workflows
- Pay-per-token pricing with no minimum commitments
- Optimized performance for large-scale inference
3. Power BI Databricks Connector M2M OAuth Support (August 2025)
Description: The new Machine-to-Machine (M2M) OAuth authentication revolutionizes how Power BI connects to Databricks. This advancement phases out risky personal access tokens in favor of secure, automated service principal-based access.
Enterprise reporting teams now enjoy improved credential management and enhanced security posture. The integration streamlines authentication workflows while maintaining strict governance controls.
Security improvements:
- Service principal-based authentication
- Elimination of personal access token dependencies
- Automated credential rotation capabilities
- Enhanced audit trails for compliance
What are the popular Databricks AI features?
AI/BI Genie
Genie transforms business intelligence. Simply ask questions in plain English. Get insights without dashboards, code, or technical expertise.
This isn’t basic keyword matching. Genie leverages context-aware, domain-specific intelligence that understands your business terminology and data relationships. Sales teams can ask “What drove our Q3 revenue spike?” and receive detailed breakdowns with visual charts.
The platform ensures governance automatically. Unity Catalog manages access permissions and privacy controls, so sensitive data stays protected while empowering self-service analytics.
Core capabilities:
- Natural language to SQL conversion
- Automatic chart and dashboard generation
- Context-aware business intelligence
- Self-service analytics for non-technical users
Availability by pricing plans
- Included: Enterprise and Advanced Databricks platform tiers
- Premium features: Data governance through Unity Catalog requires premium plans
- Note: Basic querying available on lower tiers, but governance features are essential for enterprise use
Windsor.ai No-Code ELT Connectors
Data silos kill AI projects. Windsor.ai’s no-code ELT connectors eliminate this problem by syncing data from 325+ sources directly to Databricks.
The platform handles the complex orchestration of data pipelines, incremental loading, and distributed architecture for scalability. Marketing teams can connect HubSpot, Salesforce, and Google Analytics. Finance teams can integrate QuickBooks and NetSuite. All without writing code.
Enterprise features:
- Audit logging for compliance tracking
- Automated data pipeline management
- Incremental data loading for efficiency
- Multi-cloud support for global deployments
Availability by pricing plans
- Basic features: Available in Enterprise Databricks connector plans
- Advanced automation: Multi-cloud support requires Windsor.ai tier upgrades
- Enterprise features: Full audit logging and governance controls on premium tiers
Lakebase Postgres Layer
Operational data meets analytics. Lakebase introduces a serverless, Postgres-compatible OLTP database layer natively inside Databricks lakehouse architecture.
This breakthrough eliminates architectural sprawl. Run operational workloads and real-time analytics in the same environment. Build agentic workflows and AI-enabled applications without managing separate systems.
Technical advantages:
- Serverless scaling eliminates capacity planning
- Postgres compatibility supports existing applications
- Native lakehouse integration for unified analytics
- Real-time transactional and analytical workloads
Availability by pricing plans
- Available: All post-NeonDB-acquisition lakehouse environments
- Feature tiers: Additional capabilities based on selected data platform tier
- Enterprise: Full feature set with advanced governance and scaling
Mosaic AI Agent Framework
Build enterprise-ready agents in Python. The Mosaic AI Agent Framework provides an extensible structure that integrates seamlessly with LangChain/LangGraph, LlamaIndex, and custom implementations.
Prototyping starts easy with AI Playground. Deployment through MLflow Model Signatures ensures compatibility and scalability for business teams. No vendor lock-in. No proprietary frameworks.
Development workflow:
- Rapid prototyping with AI Playground
- Integration with popular agent frameworks
- MLflow deployment for production scaling
- Python-native development experience
Availability by pricing plans
- Full feature set: Advanced and Professional Databricks plans
- Basic prototyping: Available on lower tiers with limited production capabilities
- Enterprise deployment: Requires premium MLflow and governance features
Common limitations of Databricks AI
Pricing complexity hurts adoption. Users consistently report that advanced feature pricing (Mosaic Model Serving, Unity Catalog) is difficult to understand and can result in unexpectedly high bills.
“If you don’t pay for the advanced governance features, AI/BI Genie and audit logging are much less useful because you can’t restrict data access as needed.” – Reddit user, 2025
Regional feature gaps create headaches. Certain AI and connector features aren’t available in all regions. Global teams face data governance challenges and adoption roadblocks when critical features are missing.
Steep learning curve for custom agents. Building custom agents with MLflow or Mosaic requires substantial Python and data engineering expertise. The documentation gaps make debugging particularly painful.
“I struggled with the model signature requirements—they aren’t well documented, and debugging was a big pain” – Databricks Community Forum, 2025
Platform lock-in concerns grow. The tight integration between Lakebase, Genie, Agent Bricks, and other tools creates dependencies that make switching platforms or mixing tools more difficult.
“We love how fast we can launch agents, but the platform’s all-in-one approach means you’re stuck with Databricks even for things a simpler tool could handle” – Reddit thread, June 2025
What are the alternatives to Databricks AI?
Coefficient AI
While Databricks requires data engineering expertise, Coefficient brings AI directly to Google Sheets where business teams already work.
- Dashboard creation without code: Ask Coefficient to “create a sales performance dashboard” and watch it build comprehensive visualizations with charts, metrics, and insights. No Python required. No complex queries.
- Intelligent data analysis: Coefficient analyzes your spreadsheet data automatically, providing breakdowns and summaries without manual sorting or calculations. Perfect for quick insights and exploratory analysis that would take hours in traditional BI tools.
Zapier
Zapier’s AI features focus on workflow automation rather than deep analytics.
- Smart workflow suggestions: The platform analyzes your connected apps and suggests automation opportunities. Much simpler than building custom agents in Databricks.
- Natural language automation: Describe what you want automated in plain English. Zapier’s AI builds the workflow connections automatically.
Clay
Clay specializes in enhancing existing datasets with AI-generated insights.
- Automated lead research: Clay’s AI agents research prospects automatically, gathering company information, contact details, and personalized insights for sales teams.
- Smart data transformation: Transform raw data into actionable insights using AI-powered enrichment that understands business context and industry specifics.
Transform your data analysis today
Databricks offers powerful AI capabilities, but the complexity and cost can overwhelm many teams. The learning curve is steep. The pricing is confusing. The platform lock-in is real.
For teams who need immediate AI-powered insights without the infrastructure overhead, Coefficient provides a simpler path forward. Build dashboards, analyze data, and create visualizations directly in Google Sheets. No data engineering required.
Ready to experience AI-powered spreadsheets? Get started with Coefficient today and transform how your team works with data.
FAQs
Does Databricks have AI agents?
Yes, Databricks offers AI agents through Agent Bricks, which automatically creates and optimizes domain-specific AI agents for enterprise use cases. The platform handles agent creation, optimization, and deployment with minimal manual intervention. For teams seeking simpler AI integration, Coefficient provides AI-powered analysis directly in spreadsheets without requiring agent development expertise.
Which three features are available in Databricks as part of Mosaic AI?
The three core Mosaic AI features are: 1) Model Serving for deploying and hosting foundation models like GPT OSS, 2) Agent Framework for building enterprise-ready AI agents with Python integration, and 3) AI/BI Genie for natural language querying of enterprise data. These components work together to provide end-to-end AI capabilities within the Databricks platform.
What is the AI strategy of Databricks?
Databricks’ AI strategy focuses on democratizing AI through unified data and AI platforms that eliminate the complexity between data preparation and model deployment. The company emphasizes governance, security, and enterprise-grade scalability while making AI accessible to business users through natural language interfaces. Their approach centers on the lakehouse architecture that combines operational and analytical workloads.
What is the name of the AI tool in Databricks?
Databricks doesn’t have a single AI tool but rather a comprehensive suite called Mosaic AI, which includes multiple components: Agent Bricks for automated agent creation, AI/BI Genie for natural language querying, the Agent Framework for custom development, and Model Serving for foundation model deployment. Each tool serves different aspects of the AI development and deployment lifecycle.
