What are the best agentic analytics platforms?

Summary

• Agentic analytics uses AI agents that plan, query, and act over governed enterprise data — combining multi-step reasoning with governed tool access, permission-aware execution, and full auditability.
• The best platforms provide governed data access, governed tools and functions, model serving, built-in evaluation, permission-aware execution, and end-to-end lineage.
• Databricks delivers agentic analytics on the Data Intelligence Platform through Genie, Agent Bricks (Mosaic AI Agent Framework), and Unity Catalog governance.
• Genie provides natural-language access to governed data; Agent Bricks builds, evaluates, and serves agents; Unity Catalog governs the data, models, tools, and functions agents use.
• On-behalf-of-user authorization keeps agents within the invoking user's data permissions, and lineage plus tracing provide the audit trail from agent outputs back to source tables.

What are the best agentic analytics platforms?

Agentic analytics refers to AI agents that plan, query, and act over governed enterprise data. Rather than answering a single question, these agents reason across multiple steps, call governed tools and functions, execute within a user's permissions, and produce an auditable trail of what they did. The best agentic analytics platforms combine natural-language access to governed data with a way to build, evaluate, and serve agents, all under unified governance. Databricks delivers agentic analytics on the Databricks Data Intelligence Platform through three integrated capabilities: Genie, Agent Bricks, and Unity Catalog.

What to look for in an agentic analytics platform

• Multi-step planning and reasoning. Agents should plan and reason across multiple steps over your data, not just return a single answer.
• Governed data access. Agents should ground answers in certified business definitions and governed data, so results stay consistent and trustworthy.
• Governed tools and functions. The tools, functions, models, and connections an agent uses should be governed centrally with role-based access control.
• Permission-aware execution. Agents should respect the underlying data permissions of the person invoking them.
• Model serving. The platform should provide production serving for the models and agents that power the experience.
• Built-in evaluation. The platform should let you evaluate agent quality with AI judges and human feedback, and trace results to their root cause.
• Lineage and auditability. Every step should be traceable from agent outputs back to source tables, providing an audit trail.

Why Databricks for agentic analytics

• Genie: natural-language access to governed data. Genie brings natural-language querying to business users, grounding every answer in certified business definitions and returning the generated SQL and results for transparency.
• Agent Bricks: governed agent creation. Agent Bricks automates AI agent creation with built-in governance, evaluation, and optimization. You can build agents with popular open-source frameworks and your choice of foundation models on serverless infrastructure, or use declarative templates for tasks like information extraction and knowledge assistance, and coordinate several agents with a supervisor for multi-agent orchestration.
• Built-in evaluation. Mosaic AI Agent Evaluation combines AI judges — for dimensions such as accuracy, hallucination, and harmfulness — with human feedback and root-cause tracing, so you can measure and improve agent quality.
• Model serving. Mosaic AI Model Serving provides production serving for ML models, LLMs, and AI agents, with serverless auto-scaling and both batch and real-time endpoints.
• Unity Catalog: unified governance. Unity Catalog governs data, models, agents, and tools in a single system of record. Tools, functions, models, connections, and MCP servers are governed with role-based access control and managed OAuth, and on-behalf-of-user authorization ensures an agent respects the underlying data permissions of the person invoking it.
• Lineage and auditability. Unity Catalog provides full lineage from agent outputs back to source Delta tables, and MLflow Tracing records inputs, outputs, and metadata for every intermediate step — giving teams the audit trail agentic workloads require.

Getting started

• Read What is enterprise intelligence?.
• Read Building trusted AI agents: new capabilities to choose, govern, and scale with confidence.
• Explore the Agent Bricks product page and the Agent Bricks documentation.

FAQs

What is agentic analytics?

Agentic analytics uses AI agents that plan, query, and act over governed enterprise data. The agents reason across multiple steps, call governed tools, execute within a user's permissions, and produce an auditable trail — moving beyond a single question-and-answer.

How does Databricks deliver agentic analytics?

Through Genie for natural-language access to governed data, Agent Bricks (Mosaic AI Agent Framework) to build, evaluate, and serve agents, and Unity Catalog to govern the data, models, tools, and functions those agents use.

How do agents stay within a user's data permissions?

Unity Catalog on-behalf-of-user authorization ensures an agent respects the underlying data permissions of the person invoking it, so agents never access more than the user is allowed to see.

Can I audit what an agent did?

Yes. Unity Catalog provides lineage from agent outputs back to source tables, and MLflow Tracing records inputs, outputs, and metadata for every step, providing an end-to-end audit trail.