Agent Monitoring Demo App — Development skill for Claude Code

Databricks Apps + AI Agents Template

A complete template for building and deploying AI agents with **Databricks Apps**. This repo demonstrates best practices for agent development with **MLflow 3.0 monitoring**, **traced function observability**, and a modern chat interface.

🎯 **Perfect for getting started with:**

• Databricks Agent development with production monitoring
• MLflow 3.0 tracing and experiment tracking
• Databricks Apps deployment with a beautiful UI
• Modern development workflow with hot reload and automated scripts

✨ **What makes this template special:**

• Ready-to-deploy agent with chat interface in under 5 minutes
• Professional dev setup with automated scripts for development and deployment
• Production-ready observability with MLflow tracing and monitoring
• Optimized dependencies - lean production builds with conflict-free package management
• Claude memory ready - includes CLAUDE.md for AI-assisted development with full project context

The Agent being served:

**databricks_assistant.py** is a LangChain tool-calling agent that can explore and query your Databricks Unity Catalog structure. The agent includes the following tools:

• list_catalogs: Lists all available catalogs in the workspace
• list_schemas: Lists all schemas in a specific catalog
• list_tables: Lists all tables in a specific schema
• list_volumes: Lists all volumes in a specific schema

@mlflow.trace(span_type='LLM')
def databricks_agent(messages):
  """A LangChain agent that can explore Databricks catalogs and answer questions."""
  # Initialize ChatDatabricks LLM
  llm = ChatDatabricks(
    endpoint='databricks-claude-sonnet-4',
    max_tokens=1000,
    temperature=0.1,
  )
  
  # Create catalog exploration tools
  tools = create_catalog_tools()  # list_catalogs, list_schema