Error handling is one of the most important and most frequently neglected aspects of production automation. A scenario that fails silently can cause significant operational problems — missed notifications, incomplete data updates, and broken process chains — that are difficult to diagnose and fix after the fact. Make provides comprehensive error handling tools that allow you to build scenarios that recover gracefully from failures and alert the right people when issues require human intervention.
Understanding Error Types in Make
Make classifies errors into several types, each of which may warrant a different response strategy. Connection errors occur when Make cannot reach the connected app’s API. Data errors occur when the data structure or values do not match what a module expects. Timeout errors occur when an API call takes too long to respond. Rate limit errors occur when you exceed an API’s call frequency limits. Understanding which type of error is occurring is the first step in building the right error handling response in your Make scenarios.
Configuring Error Handlers
Make’s error handler modules allow you to define what happens when a module fails. The Resume handler attempts to continue the scenario with a fallback value. The Ignore handler skips the failed module and continues with the next one. The Break handler stops the scenario and marks the execution as incomplete. The Rollback handler undoes all operations completed before the error. Choose the right handler for each module based on the criticality of that module’s output and whether the scenario can produce meaningful results if that module fails.
Alerting on Errors
Configure every production Make scenario to send an alert to Slack or email when a critical error occurs. This alert should include the scenario name, the module that failed, the error message, and the data that was being processed when the failure occurred. This context makes it much faster to diagnose and fix the issue than investigating a bare error notification with no additional detail.
Logging Errors for Pattern Analysis
For high-volume scenarios, log error events to a database or spreadsheet in addition to sending alerts. This creates a historical record of error patterns that helps identify recurring issues — API endpoints that fail frequently, data quality problems that cause consistent parsing errors, or rate limits that are regularly exceeded. Use this pattern data to make informed decisions about where to invest in error prevention, such as adding retry logic, improving data validation upstream, or requesting higher API rate limits.
