Your RPA will fail at some point, and that’s a simple fact—especially when automating systems beyond your control. Instead of seeing RPA errors as a sign of weakness in your team, you should treat them as part of the process.
Handling Errors in Robotic Process Automation (RPA)
Error handling in RPA comes with unique challenges. Most of the time, we develop UI-based automations for systems that may change for various reasons. For example, web-based systems frequently receive updates, making RPA failures an inevitable part of the game.
Since it’s difficult to predict and prepare for these changes in advance, the best approach is to detect and react quickly so you can deploy an updated version of your automation with minimal downtime.
Reasons Why UI Workflows Change in RPA
In most cases, the RPA team cannot predict or be aware of system changes that will impact bots in their next execution. Here are some common scenarios:
1. New System Version
Many modern systems roll out updates automatically without user consent. They simply update functionality and UI/UX while notifying users via a release note or changelog. The goal is to continuously enhance the system without disrupting user experience.
However, if your bot automates UI actions based on component IDs or is sensitive to UI layouts, these changes can break your automation.
2. Required Updates to Access New Features
Some systems require users to manually authorize or install updates, giving IT departments greater control over system changes. This doesn’t just impact RPA—many ERPs and enterprise applications have customization layers and integrations that can be affected by system updates.
Even though keeping systems up to date is a best practice for security, performance, and new features, RPA teams in large enterprises often aren’t notified about these updates until bots start failing.
3. UI Changes Due to Customization
Many enterprise systems allow businesses to customize fields and business rules to better suit their needs. However, new customizations can easily break bots if the RPA team is unaware of them.
Departments within an organization want autonomy to maximize system benefits, but this introduces uncertainty for RPA teams, who must continuously adapt to these unexpected changes.
Detecting System Updates That Require Automation Updates
Not every system update will cause a bot failure. Likewise, not every bot failure is caused by a system update. Updating your automation to account for system changes is not the same as fixing a regular bug.
Since bots follow a predefined set of steps for a specific system version, the first step after a failure is to analyze the update’s impact and identify which parts of the automation are affected.
Understanding why the bot stopped working can significantly reduce fix time and downtime.
1. Checking If UI Components Exist
Whether your bot interacts with UI components via ID-based selection or visual recognition, it’s essential to validate component availability instead of blindly attempting actions that may fail.
If the expected form or window is present, but the target component is missing, the UI has likely changed.
2. Statistical Error Monitoring
RPA errors happen all the time due to system unavailability, invalid parameters, or other issues. However, if the number of errors suddenly spikes, it’s likely that a workflow change has occurred, requiring RPA team intervention.
Monitoring error statistics is an excellent way to detect automation workflow changes without having to anticipate every possible failure scenario. Using real-time alerting tools can significantly improve reaction time.
Reacting Fast and Reducing Downtime from System Changes
To monitor errors and respond faster, it’s essential to have your RPAs integrated with an orchestration platform.
BotCity Orchestrator provides an error monitoring module that notifies RPA developers about failures, displaying stack traces, screenshots, and execution parameters associated with errors.
For each error reported to BotCity Orchestrator, the following information is displayed:
For statistical analysis, you can use BotCity Insights, which provides success rates, error classification, and other key metrics for a specific automation or a set of automations.
Any sudden change in your error patterns or distribution may indicate a workflow change in your automation. Identifying the automations responsible for these changes and analyzing their errors is a great starting point to pinpoint the issue and resolve it as quickly as possible.
