Runs & Logs

Workflow runs and logs provide complete visibility into your automation execution, helping you monitor performance, debug issues, and ensure reliable operation of your business processes.

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Understanding Workflow Runs

A workflow run represents a single execution of your published workflow, triggered by an event and tracked from start to completion. Each run captures the execution status, timing information, trigger context, and detailed results from each step.

Workflow runs progress through distinct states as they execute. Runs start in a pending state while waiting for execution resources, move to running while actively processing steps, and end in completed, failed, or cancelled states depending on the outcome.

The system tracks comprehensive metrics for each run, including start time, duration, completion time, and the number of successful and failed steps. This information helps you understand how your workflows are performing and identify any patterns or issues.

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Step-by-Step Logging

Each step in a workflow run is individually tracked and logged with detailed information about its execution. You can see exactly what inputs each step received, what outputs it produced, and how long it took to execute.

The logs show both the raw configuration values and the resolved dynamic values, making it easy to understand how data flowed through your workflow. For example, you can see how a dynamic reference like “ticket priority” was resolved to the actual priority value from the triggering event.

Step states help you understand the execution flow. Steps progress from pending to running to completed, or they may fail if an error occurs. Some steps may enter a waiting state if they’re paused for external triggers or timers.

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Performance Monitoring

Monitor your workflows’ performance with detailed metrics including execution time, success rates, and resource usage. Track how long each step takes to execute and identify any bottlenecks or performance issues that might affect your automation efficiency.

The system provides dashboards showing key performance indicators like executions per day, average execution time, success rate percentages, and error frequency trends. This helps you understand your workflows’ reliability and identify optimization opportunities.

Performance data also helps with capacity planning by showing peak usage periods and processing patterns. Understanding when your workflows are most active helps you plan for scaling and resource allocation.

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Error Tracking and Debugging

When workflows fail, comprehensive error information is captured to help you understand what went wrong. Error details include the specific step that failed, the error message, and contextual information about the conditions that led to the failure.

Common error types include validation errors from invalid input data, integration errors from external service failures, timeout errors from steps taking too long, and permission errors from insufficient access rights. Understanding these error patterns helps you build more robust workflows.

The debugging tools let you examine input and output data at each step, trace how data flowed through dynamic references, and identify where execution diverged from the expected path. This step-by-step analysis makes it much easier to pinpoint and fix issues.

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Monitoring Best Practices

Set up alerts for workflow failures so you’re notified promptly when issues occur. Monitor execution patterns to identify trends like increasing failure rates or performance degradation that might indicate underlying problems.

Use historical analysis to understand long-term trends in your workflow usage and performance. Look for patterns like seasonal variations, peak usage periods, or gradual changes in success rates that might require attention.

When troubleshooting workflows, start by reviewing error messages and examining the execution timeline. Check step outputs to understand what data was available at each point, and verify that external integrations are working correctly.

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Systematic Debugging

Approach debugging systematically by first identifying the problem through error messages and execution logs. Examine the step outputs to understand what happened at each point in the workflow, and check that trigger conditions were met as expected.

Isolate issues by testing individual steps when possible, verifying that external integrations are available and properly authenticated, and checking that data transformations are working correctly. This helps you narrow down the root cause of problems.

Once you’ve identified and fixed the issue, test your corrections in draft mode before publishing the updated workflow. Monitor the corrected version to ensure the problem has been resolved and doesn’t recur.

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Log Management

The system retains workflow logs for analysis and compliance purposes, with different retention periods for different types of data. Run logs are kept for standard periods, while error logs may be retained longer for analysis and performance metrics are kept for long-term trending.

Access to logs is controlled by user permissions, ensuring that team members can see logs for workflows they own or manage while maintaining appropriate security. You can export execution logs, generate performance reports, and create audit trails as needed for compliance or analysis purposes.

Regular log review helps you maintain healthy workflows and catch potential issues before they become problems. Use the comprehensive logging and monitoring capabilities to ensure your workflows operate reliably and provide the visibility needed to optimize your automation infrastructure.