Build AI agents on Azure Databricks

Azure 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.

Guide	Description
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 Azure Databricks	Learn about models, agents, tools, and apps.

Concepts

Get familiar with how agents work on Azure Databricks.

Guide	Description
Concepts: Generative AI on Azure 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.
Azure Databricks generative AI capabilities	Learn about the agent and GenAI capabilities available on Azure Databricks.
Key challenges in building GenAI apps	Understand key challenges of GenAI and how Azure Databricks addresses them.

Build and deploy

Develop and deploy agents.

Feature	Description
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.
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.

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