Automated enterprise AI agent platform that builds production-grade agents optimized for knowledge retrieval, document intelligence, and governed data access across the Databricks Lakehouse.
Databricks' enterprise platform for building production AI agents — integrates with Unity Catalog for governance, MLflow for evaluation, and Vector Search for retrieval to deliver knowledge assistants that operate securely within the Lakehouse ecosystem.
Databricks Mosaic AI Agent Framework is an end-to-end platform for building, evaluating, and deploying production AI agents directly within the Databricks Lakehouse. It combines retrieval-augmented generation with Unity Catalog governance, MLflow-based evaluation, and scalable model serving to deliver enterprise-grade knowledge assistants.
The framework offers Agent Bricks — pre-built agent templates including the Knowledge Assistant with Instructed Retriever technology — that eliminate manual prompt engineering and retrieval tuning. Organizations can connect agents to documents stored in Unity Catalog Volumes or Delta tables and deploy them to production endpoints with built-in monitoring.
Databricks reports that over 10,000 organizations use the Lakehouse platform globally, and Mosaic AI agents benefit from the same infrastructure. In internal benchmarks, the Instructed Retriever approach improved retrieval relevance by 15–25% compared to standard vector-search RAG pipelines across enterprise document corpora. MLflow evaluation pipelines enable teams to track agent quality systematically, with organizations typically achieving production-ready accuracy within 2–4 weeks of initial deployment.
Agents run on serverless or provisioned compute with auto-scaling model serving endpoints. Vector Search indexes support both storage-optimized and performance-optimized configurations. The AI Gateway provides unified access to foundation models from OpenAI, Anthropic, Google, Meta, Mistral, and Databricks' own DBRX — with built-in guardrails, rate limiting, and cost tracking.
Unity Catalog enforces row-level and column-level access controls, data lineage tracking, and audit logging across all agent data access. The platform holds SOC 2 Type II, HIPAA, and FedRAMP certifications, making it suitable for regulated industries including healthcare, financial services, and government.
Consumption-based DBU pricing starts at approximately $0.07/DBU for standard compute and $0.10–$0.22/DBU for GPU Model Serving. Enterprise commitments of $50K–$100K+ annually typically unlock 20–35% volume discounts. A typical knowledge assistant handling 10,000 queries per day costs roughly $100–$500/month in serving costs, making it competitive with assembled open-source stacks that often run $500–$2,000/month at similar scale without built-in governance.
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Revolutionary approach that eliminates manual trial-and-error agent development through Instructed Retriever technology, which automatically learns optimal retrieval strategies for each domain and query pattern, improving relevance by 15–25% over standard vector-search RAG.
Pre-built agent architectures optimized for common enterprise scenarios: Information Extraction agents for structured data extraction from documents, Knowledge Assistants for Q&A over document corpora, SQL Agents for natural-language analytics, and custom agents for specialized workflows.
Deep integration with Unity Catalog that enables agents to understand enterprise context including table schemas, column descriptions, data lineage, and access policies — allowing agents to answer questions with full awareness of organizational data assets.
Access to leading AI models from OpenAI, Anthropic, Google, Meta, and open source through the AI Gateway, with intelligent routing, cost tracking, rate limiting, and guardrails applied consistently across all model providers.
Comprehensive platform for monitoring, tracing, and optimizing AI agents with integrated experiment tracking, automated evaluation datasets, LLM-as-a-judge scoring, and production quality dashboards for continuous improvement.
Advanced capability that automatically creates domain-specific synthetic data resembling production queries, enabling teams to build robust evaluation suites and stress-test agents before deployment without relying solely on manually curated test sets.
~$0.07/DBU on AWS (list); ~$0.10–$0.22/DBU for GPU Model Serving endpoints depending on instance size
Custom negotiated DBU commitment; typical 1-year commits start around $50K–$100K+ annual spend with 20–35% discount off list rates
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In 2026, Databricks expanded Mosaic AI with Agent Bricks templates for rapid deployment, Instructed Retriever technology for automated retrieval optimization, MLflow 3.0 integration with enhanced GenAI lifecycle management, and broader foundation model access through the AI Gateway including DBRX, Llama 3, and Mixtral. The platform also added synthetic data generation for evaluation and improved multi-agent orchestration support via LangGraph and CrewAI.
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