Lesson Overview

Once you’ve built your AI agent workflow, making sure it actually works is the next step. In this lesson, you’ll see what to do when things don’t go as planned during testing. Errors are a normal part of building with AI agents, so learning to diagnose and resolve them is a key skill.
This lesson focuses on a practical method: running your full workflow and seeing what happens in real-time. When things break, you’ll capture and use those error messages to get specific help—often from tools like ChatGPT. This process saves you hours of guesswork by turning errors into clear, actionable instructions.
You’ll also see how to test your workflow after making fixes, validating each change until everything works smoothly. The approach shown here can be applied to nearly any AI agent, tool, or automation you create—making your workflows more reliable in real-world use, whether you’re building for work, class projects, or personal productivity.

Who This Is For

Learning to test and troubleshoot AI agent workflows is useful for:

• Beginners setting up their first AI agent
• Educators introducing students to workflow automation
• Product builders who want to integrate AI steps into their processes
• Business users seeking to automate routine tasks
• Anyone interested in practical ways to solve errors and improve their AI projects

Where This Fits in a Workflow

Testing and troubleshooting is an essential stage after you build or edit any AI agent workflow. Once you’ve connected steps and logic, you need to verify that they actually work together. For example, after building an agent to send custom email recommendations, you’ll test the process from start to finish and work through any errors that come up. This approach ensures your automations are reliable before you or others depend on them for daily tasks.
By learning this process, you build workflows that you can trust—saving time that would otherwise go into manual checks or repeated fixes later on.

Technical & Workflow Benefits

Manually searching for solutions to workflow errors can be frustrating and slow. The method in this lesson—documenting an error and asking AI for a fix—gives you a faster path to resolution. Rather than spending time on generic forums or guessing at problems, you get targeted, relevant steps you can apply immediately. For instance, instead of re-reading documentation to guess why a weather tool failed, you simply screenshot the error, ask ChatGPT, and follow the precise recommendations.
This saves time, increases the quality and accuracy of your fixes, and prevents you from getting stuck. The approach works for new users and experienced builders alike, improving your troubleshooting skills and keeping your agent projects moving forward.

Practice Exercise

Take a workflow you’ve created (or a provided sample) and run a complete test.

1. Intentionally trigger an error by changing one step, or note where a natural error appears.
2. Screenshot or clearly copy the error message that results.
3. Paste that error into ChatGPT (or a similar tool) and ask for help resolving it.

After applying the recommended fix, re-run the workflow. Did the error resolve? What did you learn about the troubleshooting process compared to trying to resolve errors on your own?

Course Context Recap

This lesson marks your transition from building an AI agent to making sure it actually works in practice. Previously, you focused on connecting steps and inputs in your workflow. Now, you’re learning how to test the workflow and handle errors efficiently, which is a necessary skill for maintaining reliable automations. Up next, you’ll learn methods for refining and customizing your agent based on real results—continuing to improve the usefulness and polish of your workflows. To get the most value, keep following the course and try each step as you go.