Error handling is a critical component of any robust automation. Without proper error handling, automations can fail unpredictably, leading to downtime and disruption for end users. This guide will provide a comprehensive overview of advanced error handling strategies and techniques within

We will explore the fundamentals of error handling, including understanding common error types and pitfalls. Additionally, we will cover’s built-in error handling modules and how to implement custom error handling logic when needed. Logging, monitoring, and advanced error handling patterns will also be discussed.

By the end of this guide, you will have the knowledge to confidently construct automations that can gracefully handle errors of all types. Your automations will become resilient, reliable, and capable of operating smoothly regardless of the challenges encountered.

Understanding Error Types

Automation errors generally fall into a few key categories that are important to understand. Some of the most common error types include:


Timeouts occur when an API call or other operation takes longer than expected to complete. Timeouts can happen when APIs are slow to respond or there are network connectivity issues. According to SmartUI Group, timeouts are one of the most common automation errors.

Invalid Inputs

Passing invalid or unexpected data into an API or module often results in errors. Data issues like missing fields, incorrect formats, or bad values will commonly cause automations to fail.

Authorization Failures

If API credentials or access tokens become invalid or expire, calls to those APIs will start failing with authorization errors. These issues can also occur when permissions or scopes change for an integration.

Resource Limit Exceeded

APIs often have usage limits and quotas. Exceeding these limits, such as sending too many requests too quickly, will result in errors.

Bad Responses

Even if an API call succeeds, the actual response payload may contain errors, such as HTTP 5xx status codes. It’s important to check API responses for error codes and messages.

Properly categorizing errors makes them easier to handle. Once the error type is known, the appropriate resolution can be determined.

Using’s Built-In Error Handling Modules provides several robust built-in modules designed specifically for handling errors gracefully:

Error Handler Module

The Error Handler module allows you to define custom logic to execute when an error occurs in your workflow. You can route errors to specific paths to handle them in a targeted way. For example:

Error handler route · To add an error handler route to a module (let us call it Module X), right-click (or touch and hold if you are using a touch screen-device) …

Assert Module

The Assert module lets you validate data and variables to catch errors early. You can check conditions and throw errors if they evaluate to false.

Retry Module

The Retry module enables handling transient errors by retrying failed operations. You can configure the number of retry attempts and delays between attempts.

Best practices for integrating these modules include:

  • Placing Error Handler modules at the start of workflows to catch errors globally
  • Using Assert modules after key steps to validate expected output
  • Adding Retry modules around API calls and network operations to handle transient faults
  • Route errors to dedicated branches for specific handling logic
  • Setting up notifications and alerts triggered by errors

Custom Error Handling

While the built-in modules provided by are powerful, there are scenarios where you may require additional flexibility and control. This guide will take you through practical examples of how to implement custom error handling logic. We will explore the use of tools such as the Router, Webhooks, and Code modules, which can be tailored to manage complex errors with finesse.

The Router module allows you to direct the flow of your automation based on custom conditions and logic. You can leverage the Router to check for specific error types or error messages, and then route the execution to an appropriate error handling path. For example:

“If error type = API Error, go to API Error Handler path, else if error type = Timeout, go to Timeout Handler path.”

Webhooks enable you to call external endpoints when an error occurs, allowing integration with external systems for notifications or recovery logic. You can send key error details via webhooks to trigger custom error handling code in an external app.

The Code module is extremely versatile for implementing custom error handling scripts and functions using JavaScript. You can analyze the error object, parse it, and take actions like sending Slack notifications, logging to a database, etc. According to Zapier, the Code module is great for aggregating errors from multiple steps before handling them.

By combining these modules creatively, you can craft seamless error handling logic tailored to your specific automation requirements.

Logging and Monitoring for Error Handling

A comprehensive error handling strategy extends beyond merely catching and resolving errors; it also encompasses the need for visibility. We will demonstrate how to set up logging and monitoring systems that will enable you to track errors, discern patterns, and proactively address issues before they escalate into more significant problems.

Effective logging is essential for gaining insight into errors and exceptions that occur in your automations. Detailed logs allow you to reconstruct the sequence of events leading up to an error, pinpoint the root cause, and prevent the issue from recurring. According to this checklist, key best practices for logging include capturing timestamps, logging exceptions with stack traces, and logging contextual information. Standardized log formats and severity levels also help streamline analysis.

Beyond logging, implementing real-time monitoring and alerting ensures errors are detected proactively. For example, you can set up alerts when specific error thresholds are exceeded or performance degrades. This visibility enables you to address problems before they impact end users. As discussed in this error logging guide, integrating monitoring tools like Rollbar provides an automated way to aggregate and analyze error data.

With rigorous logging and monitoring practices, you can achieve true resilience for your automations. Tracking errors systematically lays the foundation for continuous improvement and risk mitigation. Strive to make errors visible so you can address them quickly and prevent larger failures down the line.

Advanced Techniques

To take your error handling to the next level, you can utilize advanced techniques like error aggregation and differentiating between transient vs permanent errors.

Error aggregation involves collecting multiple errors and handling them together rather than individually. This is useful when you need to gather all validation errors on a form submission rather than returning them one at a time. provides aggregation modules like the Collector module that allow you to group errors efficiently.

Another advanced approach is differentiating transient errors from permanent ones. Transient errors are temporary glitches like a network outage. Permanent errors indicate a fundamental issue like invalid user input. It’s best to retry after transient errors but return immediately on permanent ones. You can use’s Retry module to automatically retry transient errors.

For more examples of advanced error handling patterns, check out this comprehensive guide: Advanced error handling techniques

Error Recovery

Resuming workflow execution gracefully after an error has occurred is a hallmark of robust error handling. There are several techniques in that can facilitate recovering from errors and resuming normal workflow execution:

The Retry module is invaluable for automatically retrying a failed action after an error, avoiding the need to restart the entire workflow. The module can be configured with retry intervals and limits to retry intelligently. provides checkpoint capabilities that allow you to save the workflow state at certain steps. After an error, you can resume from the last successful checkpoint instead of restarting from scratch.

For advanced scenarios, you can build custom error handling logic with Code modules and variables to track errors and bypass failed steps to proceed with the non-erroneous parts of the workflow.

Proper error handling procedures should include a robust notification system to alert relevant stakeholders when errors occur and when normal functioning resumes after recovery.

Well-structured error handling enables seamless resumption of workflow execution, avoiding costly disruptions and delivering reliable automation.

Testing Error Handling

Thoroughly testing error handling logic is crucial to ensuring your automations are resilient. A key technique for testing involves intentionally simulating errors to validate that they are handled correctly.

For example, you can deliberately trigger a timeout error by setting a short timeout threshold. The automation should catch this error and implement the appropriate retry or error handling logic. To validate the logic, assertions can check that the retry succeeded or that the error was logged as expected.

Other ways to simulate errors include returning error codes from API calls, passing invalid parameters, disconnecting from the internet, or triggering failures in external services. The goal is to force the automation into an exceptional state so you can observe how it responds.

As advised in this article, it is also important to test boundary conditions around errors. Try values at the limits of valid ranges to ensure errors are raised properly. Test multiple concurrent errors to check handling under stress.

Robust unit tests that simulate errors in isolation are invaluable. But also test error handling in integrated end-to-end tests at a system level. This verifies handling logic works within the full automation workflow. By thoroughly testing error handling, you gain confidence in the resilience of your automations.


Thorough documentation is a critical component of effective error handling. Well-documented code enables you to easily understand where errors may arise and how they are handled. Here are some best practices for documentation related to error handling:

Always include comments explaining the purpose and logic behind your error handling code. Describe what types of errors are being caught and handled. Provide examples if applicable. Clear comments allow anyone reviewing the code to quickly comprehend the error handling strategy. Proper documentation is invaluable when debugging or modifying code later on.

Write comprehensive documentation on the errors themselves. Maintain a central repository detailing each error code, message, origin, severity, and resolution. Document any troubleshooting steps, workarounds, or tips for handling the error. Include examples of the full error response. Well-documented errors enable quick diagnosis and resolution.

Create documentation on recovery procedures. Document any steps required to resume normal functioning after an error occurs and is addressed. This allows your team to restore smooth operations rapidly after an error.

Document your logging approach for errors. Explain where logs are located, what information is captured, and how to search/analyze logs. Provide guidance on interpreting log data and identifying patterns. Thorough logging documentation empowers you to leverage your logs effectively.

Overall, comprehensive documentation demonstrates the thought and rigor behind your error handling approach. It enables your team to fully utilize your error handling system and rapidly address any issues that arise.


Effective error handling involves promptly notifying the right stakeholders when issues arise. provides several notification options to alert your team about errors through email, mobile push notifications, webhooks and more.

To enable notifications in, simply navigate to the notification settings for a workflow and specify the desired channels and recipients. Useful settings include toggling email notifications on errors and configuring webhooks to services like Slack, Microsoft Teams or PagerDuty to create incident tickets when errors occur.

When configuring notifications, it is important to provide key context about the error by including details like the error message, stack trace, affected workflow name, timestamp and other relevant metadata. This context enables faster diagnosis and resolution. You can customize notification messages and webhooks payloads to include these specifics.

Target notifications thoughtfully to avoid alert fatigue. Notify technical staff managing the workflows for all errors. But for business stakeholders, only notify on impactful errors affecting end users or critical systems. Configured judiciously, notifications enable rapid awareness and response to errors in workflows.

For more guidance, refer to’s documentation on configuring notifications.

Data Validation

One vital aspect of error handling is ensuring inputs and data are properly validated and sanitized before they enter your workflow. This helps prevent errors stemming from bad data downstream in your automation.

Some best practices for data validation include:

  • Setting expectations by providing clear input guidelines for API users or front-end forms.
  • Leveraging built-in validation modules in like Regex Match to validate field formats.
  • Sanitizing inputs by removing invalid characters, trimming whitespace, converting data types, etc.
  • Using the Assert module to validate that required fields are present.
  • Validating that inputs fall within expected ranges. also offers native integration with external data quality tools to apply more advanced validation logic. Overall, sound data validation improves data integrity and prevents avoidable runtime errors.

Exception Handling

One of the most powerful error handling techniques in is exception handling using try/catch blocks. Exceptions allow you to anticipate and handle errors gracefully within your code. The try block contains the code that may throw an exception, while the catch block handles the exception if one occurs.

For example:

try {
  // Code that may throw an exception
} catch (Exception e) {
  // Handle exception

You can create custom exception classes that extend the built-in Exception class in This allows you to throw specific exceptions that describe the error condition precisely.

For example:

class CustomException extends Exception {
  // Custom exception code

try {
  // Code that throws CustomException 
} catch (CustomException e) {
  // Handle CustomException specifically

Custom exceptions enable robust error handling by separating handling logic based on exception type. Make sure to document custom exceptions thoroughly.

For more advanced exception handling techniques, consult the documentation on exception handling.

Automated Retry

Automated retry is a crucial technique for handling transient errors in automations. The key concept is to automatically retry failed operations after waiting for a certain interval. This prevents failures from blocking the entire workflow when the error may be temporary and likely to succeed after a retry.

A common automated retry approach is exponential backoff. Here, the retry interval increases exponentially with each subsequent retry attempt. For example, could retry after 1 second, then 2 seconds, 4 seconds, 8 seconds, etc. This prevents overloading a failing resource while still providing ample retries.

We can configure automated retries in using the Retry module. The Retry module allows specifying the number of retry attempts and the delay interval. For exponential backoff, we can use a Code module to increment the delay exponentially on each retry attempt.

Automated retry is especially useful for unreliable API calls and network operations. But we must set sane limits to avoid endless futile retries. It’s also important to distinguish transient errors from permanent failures that require a different response.

Overall, automated retry with exponential backoff is an essential technique for resilient automations. It helps maintain workflow continuity through inevitable transient errors.

To learn more about automated retry best practices, check out this AWS guide: Error handling and automatic retries in AWS Lambda

Circuit Breakers

A circuit breaker is an advanced error handling pattern that can prevent cascading failures and enable graceful recovery in complex distributed systems. The concept comes from electrical engineering – a circuit breaker protects an electrical circuit from damage by cutting off power when excess current flows. In software, the circuit breaker wraps calls to external services or resources, monitors for failures, and prevents further interaction when failures reach a threshold.

Circuit breakers are especially useful for microservices and applications making frequent remote calls. For example, if Service A calls Service B, which in turn calls Service C, a failure in Service C can potentially cascade and overwhelm Service B and Service A. A circuit breaker implemented in Service B could detect the failure in Service C and open the circuit, allowing B and A to continue functioning, avoiding a system-wide outage. It also gives time for C to recover before re-enabling calls.

In, you can implement a custom circuit breaker pattern using modules like Router, Error Handler, and Retry. The circuit can monitor for error thresholds and open/close the circuit by routing requests to a fallback or denying further processing. This enables failing fast and graceful recovery from errors.

To learn more about leveraging circuit breakers in your automations, please visit my Upwork profile and contact me! I’d be happy to provide guidance as an automation integration expert.


Error handling is crucial for creating robust and reliable automations. In summary, a comprehensive error handling strategy should include:

  • Understanding common error types and pitfalls
  • Leveraging built-in error handling modules in
  • Implementing custom error handling logic when needed
  • Setting up logging and monitoring
  • Using advanced techniques like error aggregation and recovery

With proficiency in error handling, you can build automations that are resilient in the face of failures and capable of running smoothly in production environments. Handling errors gracefully is a hallmark of automation expertise.

The next step is to apply the strategies covered here to enhance your own workflows. Start by auditing your automations for potential weak points and pain points related to error handling. Then incorporate the techniques step-by-step to harden your automations against errors. Reach out to me directly at if you need help implementing advanced error handling in your automations.

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