Open the failed execution in your automation platform’s run history, identify the first step marked as failed, and inspect its error and input/output details before changing anything. Then check the failing step’s inputs, mappings, credentials, conditions, and connected service; make one targeted change; and retry only after considering whether earlier steps already changed external data.
Start with the failed execution, not the workflow canvas
Open the platform’s run, execution, or workflow history and select the specific run that did not complete as expected. A workflow canvas shows how steps are configured, but the execution record shows what happened in that particular run: which version ran, what data it received, and where processing stopped.
Check the platform’s status label before treating a run as broken. Some systems distinguish a failure from an intentional stop, a pause, a handled error, or a scheduled retry. For example, Zapier lists statuses including Errored, Safely halted, On hold, Handled error, and Scheduled. A safely halted run can reflect expected logic, not a defect. See Zapier’s run-status definitions.
Find the first failing step and capture its context
Within the failed run, locate the first step reported as errored or failed and open its details. Record the step name, exact error text, status code if shown, and timestamp or run identifier. The first failure is usually the best place to begin: later steps may simply be absent because the workflow stopped, or may show consequences of an earlier problem.
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Read the step’s inputs, request, and response
For an integration or API action, compare what the step was supposed to send with what the execution actually supplied. Look for missing required fields, blank or incorrectly mapped values, unexpected formats, and a response from the connected service. An HTTP status code narrows the investigation, but does not by itself establish whether the cause is your workflow, the connected app, credentials, or a temporary service problem.
Zapier’s HTTP logs may show the endpoint, method, parameters, headers, request body, status code, and error message. Logs are not available for every errored step; Zapier notes that required information may be missing in some cases. A missing log therefore does not prove that the step had no problem. See Zapier’s troubleshooting guide.
In GitHub Actions, begin with the run’s normal logs. If they do not explain the failure, GitHub documents extra debug logging, including step debug and runner diagnostic logging on reruns. Follow the current instructions in GitHub’s debug-logging documentation.
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Check the failing boundary before editing
Use the error and execution data to focus on the boundary where the workflow hands information to a step or service. Check the relevant items rather than changing several parts of the workflow at once:
- Inputs and mappings: Confirm required fields are present and mapped to the intended values from earlier steps.
- Conditions: Verify that filters, branches, and other logic did not route unexpected data into the action.
- Credentials and permissions: Check that the connected account is still authorized and has permission to perform the requested action.
- Connected service: Look for service-side errors or outages when the evidence points beyond your workflow. Zapier advises checking both its own and the connected service’s status pages when repeated 500 errors occur.
- Intermittency: Compare more than one run. A problem that occurs only sometimes may indicate a temporary service or network issue, while a consistent failure points more strongly to inputs, configuration, or permissions.
For an error specific to a platform or connected app, use that product’s current documentation; a status code or short message may not contain enough detail to identify the cause on its own.
Test one likely fix with the failed run’s data
Change one suspected cause at a time, then test with controlled data or the prior execution’s data if the platform offers it. This makes it easier to tell whether the change addressed the error and avoids introducing a second problem while troubleshooting.
In n8n, failed executions can be loaded into the editor for debugging and rerun with the prior execution data. Availability depends on the n8n hosting plan. The n8n debugging documentation describes the process and its plan limitation.
Retry or replay only after checking for side effects
A retry can resolve an intermittent failure, but replaying a whole run may also repeat steps that succeeded before the error. Before retrying, consider whether an earlier step created a record, sent a message, charged a payment, or otherwise changed an external system. The platform documentation does not guarantee that every workflow is safe to replay without duplicates. Check the downstream system and confirm the intended result after the retry.
Zapier
Zapier documents replaying failed runs and Autoreplay for certain temporary issues, such as a brief API outage or server timeout. Review the errored step and any prior actions before replaying. If a Zap errors repeatedly, Zapier Help Center documentation states, “If a Zap errors repeatedly, it will automatically turn off.” See Zapier’s troubleshooting guide and its status explanations.
n8n
n8n’s Executions list can be filtered by workflow, status, and start time. A failed execution can be retried using either the currently saved workflow or the original workflow, together with the prior execution data. Those choices matter: the saved workflow may have changed since the run, while retrying the original uses the workflow version from that execution. Consult n8n’s executions documentation for the available execution and retry behavior.
GitHub Actions
For a GitHub Actions failure, inspect the workflow run logs first. If they are inconclusive, use GitHub’s documented debug logging options on a rerun to collect step or runner diagnostics. See Enabling debug logging.
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If the failure keeps happening
When the same step fails again, preserve its exact error text and run identifier, then gather more evidence or ask the relevant platform or connected-app support team. Use HTTP logs where available, enable diagnostic logging when documented, and consult the service’s error documentation. Before sharing logs or payloads, remove credentials, access tokens, and sensitive personal or business data.
Execution retention, filtering, permissions, and access to deeper debugging features differ across platforms and plans. In n8n, both execution availability and past-run debugging depend on workflow settings or plan availability; GitHub Actions provides additional diagnostics through its documented logging options.
Quick Recap
Quick troubleshooting checklist
- Open the specific execution and confirm its status means failure rather than a pause, handled error, or expected stop.
- Identify the first failed step and note its error, timestamp, and run identifier.
- Inspect the step’s input values and, when available, its request and response details.
- Check mappings, conditions, permissions, credentials, and the connected service.
- Change one likely cause and test with controlled or prior-run data.
- Before replaying, check whether earlier actions already affected external systems; verify downstream results afterward.
- If the failure repeats, add diagnostic logging where supported and share only redacted evidence with support.
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