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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AI Agent Builders
The industry-standard framework for building production-ready LLM applications with comprehensive tool integration, agent orchestration, and enterprise observability through LangSmith.
AI Agent Builders
Open-source Python framework that orchestrates autonomous AI agents collaborating as teams to accomplish complex workflows. Define agents with specific roles and goals, then organize them into crews that execute sequential or parallel tasks. Agents delegate work, share context, and complete multi-step processes like market research, content creation, and data analysis. Supports 100+ LLM providers through LiteLLM integration and includes memory systems for agent learning. Features 48K+ GitHub stars with active community.
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