Build AI agents on Databricks

Databricks supports building, evaluating, and deploying AI agents, from simple LLM calls and Retrieval Augmented Generation (RAG) chatbots to tool-calling agents and multi-agent systems. These guides cover the concepts, development workflows, and tools you use to ship an agent.

Get started

Try a quickstart or learn the foundational concepts.

• • AI Playground
  • Prototype and test agents and LLMs with no-code prompt engineering and parameter tuning.
• • Get started with AI agents
  • Build and deploy your first AI agent end-to-end.
• • Concepts: Generative AI on Databricks
  • Learn about models, agents, tools, and apps.

Concepts

Get familiar with how agents work on Databricks.

• • Concepts: Generative AI on Databricks
  • Learn about models, agents, tools, and apps.
• • Agent system design patterns
  • Compare options and trade-offs for agent designs, from simple chains to complex multi-agent systems.
• • Databricks generative AI capabilities
  • Learn about the agent and GenAI capabilities available on Databricks.
• • Key challenges in building GenAI apps
  • Understand key challenges of GenAI and how Databricks addresses them.

Build and deploy

Develop and deploy agents.

• • Agent development lifecycle
  • Understand the full lifecycle of building an AI agent.
• • Agent Framework
  • Build and deploy agents, including RAG applications and multi-agent systems, with Python.
• • Knowledge Assistant
  • Build and optimize domain-specific QA agent chatbots.
• • Supervisor Agent
  • Build a supervisor agent that orchestrates Genie Spaces, agent endpoints, Unity Catalog functions, MCP servers, and custom agents.
• • Guide: RAG
  • Build a Retrieval Augmented Generation (RAG) system end-to-end.

Query and serve

Query LLMs and serve agents and models on scalable endpoints.

• • Query LLMs and agents on Databricks
  • Query LLMs and agents from notebooks, SQL, and applications.
• • Foundation Models
  • Serve LLMs through scalable APIs with built-in governance and monitoring.