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Tracing with Databricks Model Serving

When you deploy GenAI applications or agents that have been instrumented with MLflow Tracing through the Mosaic AI Agent Framework, MLflow Tracing works automatically without any additional configuration.

Automatic Trace Collection​

When deployed, your instrumented application will automatically emit traces to MLflow Experiments. This means:

  • No additional configuration required - If your code uses MLflow Tracing decorators, context managers, or autologging, traces are captured automatically in production
  • Same code for development and production - The same tracing instrumentation works seamlessly across environments
  • Traces appear in MLflow Experiments - Production traces are stored alongside your development traces for easy comparison

Viewing Production Traces​

Once deployed, you can view traces from your production agent in the MLflow Experiments UI, just like traces from development. These production traces provide valuable insights into:

  • Real user queries and agent responses - See exactly what users are asking and how your agent responds
  • Quality insights from user feedback - View thumbs up/down ratings, comments, and other feedback attached to production traces
  • Error rates and failure patterns - Identify when and why your agent fails
  • Behavioral patterns - Understand how users interact with your agent and identify improvement opportunities
  • Latency and performance metrics - Monitor response times and system performance in production
  • Resource usage and costs - Track token consumption and associated costs

Production Traces UI

Next steps​

Continue your journey with these recommended actions and tutorials.

Reference guides​

Explore detailed documentation for concepts and features mentioned in this guide.

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