Testing Your Agent

Overview

You have built your agent. Topics are in place, triggers are configured, and maybe you have connected knowledge sources for generative answers. Everything looks right in the authoring canvas. But looking right and working right are two different things.

Testing is where you discover the gaps between what you intended and what actually happens when real users interact with your agent. An untested agent deployed to a government workforce will encounter inputs you never anticipated, reveal trigger conflicts you did not notice, and frustrate users who expected accurate, helpful responses.

This video walks you through a systematic approach to testing your Copilot Studio agent before it reaches your users.

What You’ll Learn

• Test chat: How to use the built-in test panel to validate agent behavior
• Conversation paths: How to systematically test every topic and edge case
• Issue identification: How to spot and fix common problems
• User validation: How to gather feedback from real users before launch

Script

Hook: Test before you deploy

An untested agent is a liability, especially in government where your agent represents the agency and users expect accurate, professional interactions. Testing is where you catch the gaps, fix the errors, and build confidence that your agent is ready for real users.

You might be tempted to skip this step and go straight to deployment. Do not. The time you invest in testing saves hours of troubleshooting and user frustration later.

In the next six minutes, you will learn a systematic approach to testing your agent that covers the built-in test chat, conversation path validation, and real-user feedback.

Using the built-in test chat

Copilot Studio includes a built-in test chat that lets you interact with your agent exactly as a user would. Open it by clicking the test icon at the bottom of the workspace. A chat panel appears where you type messages and see your agent’s responses in real time.

The test chat does more than just show responses. It includes a topic tracker that displays which topic was triggered and which conversation node is currently active. You can see variable values as they are set during the conversation. This visibility is what makes the test chat a debugging tool, not just a preview window.

Start your test session by triggering the greeting experience. Type “hello” or “hi” and confirm the welcome message appears correctly and communicates the agent’s purpose. Then move to your authored topics one by one. Type a trigger phrase for each topic and verify it activates. Check that the conversation flow plays out correctly from start to finish.

If your agent has generative answers enabled, test those too. Ask questions that should be answered from your connected knowledge sources. Verify the answers are accurate and include citations. Compare the generated answers to the actual source content to confirm they are grounded correctly.

You can reset the conversation at any time to test from a clean state. Use the reset button between test scenarios so previous context does not affect your results. And remember, you can test at any point during development. You do not need to finish building to start testing.

Testing conversation paths

Effective testing requires a systematic approach, not random typing. Create a test plan before you start. List every topic in your agent, the trigger phrases you expect to activate each one, and the expected outcome for each conversation path.

Start with the happy path for each topic. The happy path is the ideal scenario where the user provides exactly the right input and the conversation flows smoothly to completion. If the happy path does not work, nothing else will either.

Then test for specific quality dimensions. Test trigger accuracy by typing variations of trigger phrases for each topic and confirming the correct topic fires every time. Test flow completion by walking through each conversation branch to its end and confirming no path leads to a dead end. Test variable handling by verifying that values collected from users are stored and displayed correctly. Test topic handoffs by following redirects between topics and confirming variables pass through.

Edge case testing is where you find the real problems. Try misspelled words and abbreviations. Type out-of-scope questions to verify the fallback topic handles them well. Send empty messages or very short inputs like a single word. Send very long, complex messages to see how the agent handles them.

For government agents, add specific test scenarios. Use agency acronyms and jargon that your users would use. Try requesting sensitive information to verify the agent handles it appropriately. Test every escalation path to confirm it routes correctly and provides the right contact information.

Identifying and fixing issues

Testing reveals issues. Here are the most common ones and how to address them.

When the wrong topic is triggered, it means trigger phrases overlap between topics. Two topics are competing for the same input. Review the trigger phrases in both topics and make them more specific. Remove or rephrase triggers that are too similar across different topics.

When no topic is triggered and the user hits the fallback, your trigger phrase coverage is insufficient. The user is asking about something your agent handles, but the phrasing does not match any triggers. Add more trigger phrase variations to the relevant topic. Pay attention to the specific wording that failed and add it.

When a conversation reaches a dead end, meaning the agent stops responding or gets stuck, you have a missing branch or an incomplete flow. Open the topic in the authoring canvas and trace the path. Look for condition branches that do not have a response, or question nodes that do not handle all possible answers.

When variable values are wrong, the entity extraction is not working as expected. Check the entity type assigned to the question node. Test with different input formats to see where extraction fails. You may need to adjust the entity configuration or add a custom entity.

When generative answers are poor or inaccurate, the problem is usually in your knowledge sources. Review the content the agent is drawing from. Is it up to date? Is it well structured? Does it actually contain the information needed to answer the question? Improve the source content and the generated answers will improve.

Use the topic tracker as your primary debugging tool. Follow the green highlights that show what matched. Check variable values at each step. Identify exactly where the conversation diverged from what you expected. Then make targeted fixes and re-test immediately to verify the fix works.

Validating with real users

Builder testing catches technical issues. Real-user testing catches everything else. You built the agent, so you test it the way you expect it to be used. Real users test it the way they actually use it, which is often different.

Set up a small pilot test before broad deployment. Select five to ten people who represent your target audience. Include users from different roles, offices, and technical skill levels. If your agent serves both technical staff and non-technical staff, include both.

Share the agent through a test channel. You can publish to a test Teams channel or a web page that only your pilot group can access. Ask testers to use the agent for real tasks, not hypothetical ones. If your agent handles IT support, have them actually try to get help with real IT questions they have.

Collect structured feedback. Ask testers to note when the agent gave an incorrect answer, when it did not understand their question, when the response was confusing or unhelpful, and when they wished the agent could do something it cannot. Also ask what they liked and what worked well so you know what to keep.

Watch for patterns. If three out of five testers ask about a topic you did not build, that is a signal to add it. If multiple testers use a phrase that does not trigger any topic, add it to your triggers. If testers are confused about what the agent can do, improve your greeting message.

For government environments, include testers from different parts of your organization. A headquarters employee may use different terminology than a field office employee. A senior executive may phrase requests differently than a new hire. The broader your pilot group, the more vocabulary and interaction patterns you capture.

Close: Quality agents through testing

Let us recap. Systematic testing catches issues before your users do. The built-in test chat gives you visibility into topic activation, variable values, and conversation flows. Conversation path testing with a structured test plan ensures every topic, trigger, and branch works correctly. And real-user validation reveals the gaps that builder testing alone cannot find.

Together, these three layers form a complete testing approach that gives you confidence your agent is ready for deployment.

Here are your next steps. Create a test plan that covers all your topics and the most likely edge cases. Run through your test plan in the test chat, fixing issues as you find them. Then recruit a small pilot group of five to ten users to validate the experience before you launch to your broader audience.

The time you invest in testing saves your users from frustration and your agency from embarrassment. Test thoroughly, fix what you find, and deploy with confidence.

Sources & References

• Test your agent in Copilot Studio — Official documentation for the test chat panel, topic tracking, and debugging tools
• Microsoft Copilot Studio documentation — Comprehensive documentation hub for all Copilot Studio capabilities
• Build great bots with Copilot Studio — Microsoft’s guidance on quality assurance and testing best practices for agents