Mosaic AI Agent Framework: Author a custom schema OpenAI agent

This notebook demonstrates how to author a OpenAI agent that's compatible with Mosaic AI Agent Framework features. In this notebook you learn to:

• Author a tool-calling OpenAI agent wrapped with ChatAgent with custom inputs and outputs
• Manually test the agent's output
• Log and deploy the agent

To learn more about authoring an agent using Mosaic AI Agent Framework, see Databricks documentation (AWS | Azure).

Prerequisites

• Address all TODOs in this notebook.

Define the agent in code

Define the agent code in a single cell below. This lets you easily write the agent code to a local Python file, using the %%writefile magic command, for subsequent logging and deployment.

Test the agent

Interact with the agent to test its output. Since we manually traced methods within ChatAgent, you can view the trace for each step the agent takes, with any LLM calls made via the OpenAI SDK automatically traced by autologging.

Replace this placeholder input with an appropriate domain-specific example for your agent.

Log the agent as an MLflow model

Log the agent as code from the agent.py file. See MLflow - Models from Code.

Pre-deployment agent validation

Before registering and deploying the agent, perform pre-deployment checks using the mlflow.models.predict() API. See Databricks documentation (AWS | Azure).

Register the model to Unity Catalog

Before you deploy the agent, you must register the agent to Unity Catalog.

• TODO Update the catalog, schema, and model_name below to register the MLflow model to Unity Catalog.

Deploy the agent

Next steps

After your agent is deployed, you can chat with it in AI playground to perform additional checks, share it with SMEs in your organization for feedback, or embed it in a production application. See Databricks documentation (AWS | Azure).