Prototype tool-calling agents in AI Playground
This article shows how to prototype a tool-calling AI agent with the AI Playground.
Use the AI Playground quickly create a tool-calling agent and chat with it live to see how it behaves. Then, export the agent for deployment or further development in Python code.
To author agents using a code-first approach, see Author AI agents in code.
Requirements
Your workspace must have the following features enabled to prototype agents using AI Playground:
- Either pay-per-token foundation models or external models. See Model serving regional availability
Prototype tool-calling agents in AI Playground
To prototype a tool-calling agent:
From Playground, select a model with the Tools enabled label.
Select Tools and select a tool to give to the agent. For this guide, select the built-in Unity Catalog function,
system.ai.python_exec. This function gives your agent the ability to run arbitrary Python code. To learn how to create agent tools, see AI agent tools.
Chat to test out the current combination of LLM, tools, and system prompt and try variations.
Export and deploy AI Playground agents
After prototyping the AI agent in AI Playground, export it to Python notebooks to deploy it to a model serving endpoint.
Click Export to generate the notebook that defines and deploys the AI agent.
After exporting the agent code, a folder with a driver notebook is saved to your workspace. This driver defines a tool-calling LangGraph ChatAgent, tests the agent locally, uses code-based logging, registers, and deploys the AI agent using Mosaic AI Agent Framework.
Address all the TODO's in the notebook.
Note
The exported code might behave differently from your AI Playground session. Databricks recommends running the exported notebooks to iterate and debug further, evaluate agent quality, and then deploy the agent to share with others.
Develop agents in code
Use the exported notebooks to test and iterate programmatically. Use the notebook to do things like add tools or adjust the agent’s parameters.
When developing programmatically, agents must meet specific requirements to be compatible with other Databricks agent features. To learn how to author agents using a code-first approach, see Author AI agents in code