Accelerate End-to-End Multi-Agents on Databricks and DSPy

A production-ready GenAI application is more than the framework itself. Like ML, you need a unified platform to create an end-to-end workflow for production quality applications.

Below is an example of how this works on Databricks:

• Data ETL with DLT and jobs
• Data storage for governance and access with Unity Catalog
• Code development with Notebooks
• Agent versioning and metric tracking with MLflow and Unity Catalog
• Evaluation and optimizations with Mosaic AI Agent Framework and DSPy
• Hosting infrastructure with monitoring with Model Serving and AI Gateway
• Front-end apps using Databricks Apps

In this session, learn how to build agents to access all your data and models through function calling. Then, learn how DSPy enables agent interaction with each other to ensure the question is answered correctly. We will demonstrate a chatbot, powered by multiple agents, to be able to answer questions and reason answers the base LLM does not know and very specialized topics.