Master Databricks Mosaic AI Agent Framework with our step-by-step tutorial, detailed feature walkthrough, and expert tips.
Explore the key features that make Databricks Mosaic AI Agent Framework powerful for ai agent frameworks workflows.
Combines rule-based assertions, LLM-as-judge scoring, and human review in a single dashboard. You define pass/fail criteria, run evaluation sets against agent versions, and compare quality metrics across deployments before promoting to production.
QA teams validating that an internal knowledge-base agent returns accurate answers before rolling it out to 5,000 employees
Agents query Delta tables, Unity Catalog assets, and vector indexes natively, without ETL pipelines or API wrappers. Data stays in place, governed by existing access controls.
A financial services firm building a compliance agent that searches 10 years of transaction records stored in Delta Lake
Mosaic AI agents can call external MCP-compatible tools (client mode) and expose Lakehouse capabilities to external agents (server mode). This bidirectional support connects Databricks agents to the broader MCP ecosystem.
An operations agent that pulls inventory data from your Lakehouse and triggers reorders through an MCP-connected ERP system
A managed proxy layer that handles rate limiting, per-user cost tracking, model routing, and access control for all agent inference requests. Supports external models (OpenAI, Anthropic) alongside Databricks-hosted models.
Platform teams managing 20 internal agent projects that need shared rate limits and per-team billing
Mosaic AI is part of the Databricks platform and uses DBU-based pricing. Foundation model serving starts at $0.07 per DBU. Serverless SQL for agent analytics runs up to $0.70 per DBU. Total cost depends on inference volume, retrieval frequency, and compute tier. There is no flat monthly agent fee. Contact Databricks sales for a cost estimate based on your expected workload.
No. The Agent Framework is tightly integrated with the Databricks Lakehouse, Unity Catalog, and Model Serving. It is not available as a standalone product. If you are evaluating agent frameworks without an existing Databricks investment, platforms like LangChain, CrewAI, or AWS Bedrock Agents have lower entry barriers.
Model Context Protocol (MCP) is a standard for connecting AI agents to external tools. Mosaic AI supports MCP as both client (your agents can call external tools) and server (external agents can access your Lakehouse). This enables multi-platform agent architectures where Databricks handles data-heavy reasoning while other systems handle actions like sending emails or updating CRMs.
Agent Evaluation lets you define quality criteria using three methods: rule-based checks (response length, format compliance), LLM-as-judge scoring (an LLM grades agent responses for accuracy and relevance), and human review (team members rate responses in an integrated UI). You run evaluation sets against agent versions and compare scores before deploying to production.
Now that you know how to use Databricks Mosaic AI Agent Framework, it's time to put this knowledge into practice.
Sign up and follow the tutorial steps
Check pros, cons, and user feedback
See how it stacks against alternatives
Follow our tutorial and master this powerful ai agent frameworks tool in minutes.
Tutorial updated March 2026