Product area
Agents
Agent Framework and Agent Bricks: building, evaluating, and deploying agents on governed data.
Concepts
- 1Agents on DatabricksAn agent is a program that decides what to do. It takes a request, chooses which tools to call and in what order, reads the results, and produces an answer. The model does the choosing; everything else is ordinary software.Intermediate
- 2Agent tools as Unity Catalog functionsA tool is a function the model may decide to call in the middle of answering. On Databricks the durable place to keep one is Unity Catalog: you register a Python or SQL function, it gets a three-level name, an owner, a comment and grants, and from then on it…Advanced
- 3Evaluating agentsEvaluating an agent means running it against a set of representative inputs and scoring the outputs with scorers — some are LLM judges, some are plain code — instead of eyeballing transcripts. mlflow.genai.evaluate() is the entry point: give it an agent (or a…Advanced
- 4Agent memoryManaged agent memory gives an agent something to remember between conversations. Databricks runs the storage and the isolation; you get a memory store, which is a Unity Catalog securable holding entries.Advanced Beta
- 5Agent Bricks: Knowledge Assistant and Supervisor AgentTwo builders you configure instead of code, both reached from Agents in the workspace sidebar, both producing an agent endpoint you can query from the AI Playground, from an app, or over the API.Intermediate
- 6Deploy an agent on Databricks AppsDatabricks documents one way to ship a custom agent: write it as an ordinary Python project, then run that project as a Databricks App. Two pieces are fixed and the rest is yours.Advanced
- 7Model Context Protocol on DatabricksThe Model Context Protocol is an open standard for connecting an agent to tools, resources and prompts. On Databricks it is the documented default way to give an agent capabilities, and a server comes from one of exactly three places:Advanced Preview
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