Enterprise AI agent framework built into the Databricks Lakehouse, with MLOps, evaluation tooling, governance, and MCP support for building production agents on proprietary data.
Enterprise AI agent framework built into Databricks Lakehouse with MLOps, evaluation, governance, and MCP support for data-driven agents.
Enterprise AI agent framework built into the Databricks Lakehouse, with MLOps, evaluation tooling, governance, and MCP support for building production agents on proprietary data.
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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.
Use Case:
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.
Use Case:
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.
Use Case:
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.
Use Case:
Platform teams managing 20 internal agent projects that need shared rate limits and per-team billing
From $0.07/DBU
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View Pricing Options →Organizations already on Databricks that need agents querying terabytes of proprietary Lakehouse data with full governance and access control.
Financial services, healthcare, and government teams requiring audit trails, Unity Catalog lineage tracking, and compliance controls for every agent interaction.
MLOps teams running agents that combine retrieval, classical ML scoring, and LLM generation in a single governed pipeline with integrated evaluation.
Enterprises using MCP to connect Lakehouse agents with external tools, CRMs, and third-party agent frameworks while maintaining centralized governance.
We believe in transparent reviews. Here's what Databricks Mosaic AI Agent Framework doesn't handle well:
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.
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