⚠️ Module 5: Error Handling

Understand how to handle workflow errors when it is pushed into production

Introduction: Why Error Handling Matters

• Checking for execution history will be hard if you have multiple workflow in production

• Receiving notification when error happens will let you take action immediately

• Logging your error and studying it will help you improve in creating more robust workflow

1. Error Notification and Logging

• Create the error logging and notification workflow

• Set is as the workflow to run when error is detected in your other workflow in production

2. Retry on Error

• Helpful when there are rate limits

• Set a waiting time when error happens and the workflow will try it again

• Set maximum tries

3. Fallback Model

• AI models sometimes experience down time or maybe we exceeded the usage limit

• Adding fallback model will give the AI Agent another choice to use as it’s second brain

4. Continue on Error

• Helpful if you have multiple input that needs to process one at a time

• You will still receive the rest of the request except for the items that have error

• Also there is an option to pass on extra error output