Based on the provided context, it appears that there isn't a direct way within Databricks to dynamically set job tags at runtime based on a parameter passed to the job. However, there are alternative approaches you can consider to work around this limitation:

1. Job Parameters and Dynamic Value References:

   • Databricks allows passing parameters to jobs via key-value pairs. These parameters can be accessed in the notebook or task using utilities like dbutils.widgets. While this doesn’t directly set job tags dynamically, you can reference these parameters in your job logic.

2. Custom Tagging in Notebooks:

   • As per the retrieved documentation on configuring jobs, you can add or edit job tags manually in the workspace UI under the Job details panel. These tags act as static metadata for filtering or monitoring but won't change dynamically after the job starts running.

3. Using Databricks Asset Bundles (DAB):

   • If you configure your job using Databricks Asset Bundles (DAB), you might specify a template that includes static tags in its configuration. However, dynamic runtime adjustment of tags is not mentioned in the available resources.

4. External Solutions using REST API:

   • Post-job completion, you can use the Databricks REST API to update tags programmatically. A script or service external to the job can evaluate runtime parameters or outcomes and call the API to apply tags accordingly. This would not be "real-time" but would give you tagged metadata after the execution.