---
title: MLflow Tracing - GenAI observability | Databricks on AWS
source: https://docs.databricks.com/aws/en/mlflow3/genai/tracing/
ingestedAt: "2026-06-18T08:16:22.176Z"
---

MLflow Tracing is a powerful feature that provides **end-to-end observability** for GenAI applications, including complex agent-based systems. It records inputs, outputs, intermediate steps, and metadata to give you a complete picture of how your app behaves.

![Tracing Gateway Video](https://assets.docs.databricks.com/_static/images/mlflow3/tracing/tracing-top.gif)

Tracing allows you to:

*   Debug and understand your application
*   Monitor performance and optimize cost
*   Monitor production applications
*   Evaluate and enhance application quality
*   Ensure auditability and compliance
*   Integrate tracing with many popular third-party frameworks
*   Use natural language with Genie Code to analyze, debug, and explore trace data

## Next steps[​](#next-steps "Direct link to Next steps")

*   [10-minute tracing demo](https://docs.databricks.com/aws/en/mlflow3/genai/getting-started/tracing/tracing-notebook) - Get started with tracing in Databricks.
*   [Instrument your app](https://docs.databricks.com/aws/en/mlflow3/genai/tracing/app-instrumentation/) - Choose between automatic and manual tracing approaches
*   [Genie Code for agent observability and evaluation](https://docs.databricks.com/aws/en/mlflow3/genai/getting-started/genie-code) - Analyze traces, debug errors, and explore evaluation data using natural language.
