Executions & Monitoring

Executions and monitoring allow you to track how your workflows run, understand what happens at each step, and quickly identify and fix issues.

Execution Statuses

Every time a workflow runs, it creates an execution record with a status.

Syncaut uses the following statuses:

Running
The workflow is currently executing. Nodes are being processed and data is flowing through the workflow.

Success
The workflow completed successfully. All nodes executed as expected without errors.

Failed
The workflow encountered an error and stopped before completing. One or more nodes did not execute successfully.

These statuses help you quickly understand the health of your automation.

Viewing Execution Output & Logs

Each execution provides detailed insight into what happened during the run.

You can view:

• Input and output data for each node

• The order in which nodes executed

• Responses from external services (APIs, integrations)

• Logs generated during execution

This level of visibility allows you to trace how data moves through your workflow from start to finish.

Execution logs are especially useful for:

• Verifying that automation is working correctly

• Understanding how data is transformed

• Auditing past workflow activity

Debugging a Failed Execution

When a workflow fails, Syncaut provides tools to help you identify and resolve the issue.

To debug a failed execution:

1. Open the execution record

2. Identify the node where the failure occurred

3. Review thworkflows/cmmzi0xwo0000dz4ab7dvzm6te error message and logs

4. Check the input and output data for that node

5. Verify credentials, variables, and configuration

6. Fix the issue in the workflow

7. Re-run the workflow to test

Common causes of failure include:

• Invalid or missing credentials

• Incorrect input data

• API errors from external services

• Misconfigured nodes or logic

By analyzing execution logs and node data, you can quickly pinpoint the problem and restore your workflow.

Monitoring executions ensures your automations remain reliable, predictable, and easy to maintain as they scale.