> For the complete documentation index, see [llms.txt](https://cortex-docs.paloaltonetworks.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://cortex-docs.paloaltonetworks.com/cortex-cloud-runtime-security/cases-and-issues/investigation-and-response/automation/playbooks/build-your-playbook/test-your-playbook/troubleshoot-playbook-performance.md).

# Troubleshoot playbook performance

You can analyze playbook metadata such as tasks input and output, the amount of storage each task input/output uses, and the type of task. This is useful when troubleshooting your custom playbook if your system has slowed down and is using high CPU usage, memory, or storage (disk space).

### Get data from XQL datasets

You can leverage XQL for flexible and adjustable playbook and script tracking to provide performance and execution data for debugging. The following datasets are available for querying and dashboards:

* playbook\_tasks: Data about failed tasks and tasks that were retried.
* playbook\_runs: Data about playbook runs and statuses.
* scripts\_and\_commands\_metrics: Data about scripts and commands used in playbook tasks.

### Get playbook metadata using the CLI

After an issue has been assigned to a playbook you can analyze it to see its tasks inputs/outputs storage. You can filter the data according to the KB used in each task input/output.

From the Cases & Issues → Cases page, in the Case War Room tab the following command in the CLI.

`!getInvPlaybookMetaData issueId=`*`<issue ID>`*` `` ``minSize= `*`<size of the data you want to return in KB. Default is 10>`*

To view the playbook metadata that is used in issue number 964, in the CLI type `!getInvPlaybookMetaData incidentid=”964” minSize=”0”!getInvPlaybookMetaData incidentid=”964” minSize=”0”.`

### Troubleshooting Playbooks dashboard

From the Troubleshooting Playbooks dashboard, you can view playbook and task errors, average playbook run time, and execution by status for manual and automated tasks. You can also pivot to the XQL view for more detailed data analysis.

### Increase the execution timeout for AI prompt tasks

AI prompt tasks have a default execution timeout of 10 seconds. If an AI prompt task returns a **`504 Error`** with the status **`DEADLINE_EXCEEDED`**, it is likely due to the task requiring more time to process than the default setting allows. To resolve this, open the Task Details pane of the AI task and select the Advanced tab, then increase the Execution timeout (seconds) value based on the complexity of the prompt and the expected length of the LLM response. For example, for complex tasks such as generating a full vulnerability report, you many need to increase the timeout from the 10 second default to 120 seconds or higher.


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