Testing AI agents before launch is a crucial step to ensure they function reliably and perform as expected under different conditions. By understanding AI algorithms and developing comprehensive test cases, brands can identify potential issues early, improving customer satisfaction and minimizing risks.
1. General Information:
Test Case ID: Unique identifier for each test case.
Test Objective: Clear description of what the test aims to verify (e.g., order tracking, product recommendation).
Priority: Assign priority (High, Medium, Low).
Preconditions: Specific conditions that must be met before running the test (e.g., user must be logged in).
Tested By: Name of the tester responsible.
2. Test Categories:
Functional Testing
Test Case Name: Functionality (e.g., "Order Status Query").
Test Input: User query or action (e.g., "Where is my order?").
Expected Outcome: The agent correctly provides the order status.
Actual Outcome: What actually happened during the test.
Pass/Fail: Did it meet expectations?
Comments: Notes for improvement.
| Test Case ID |
Test Name |
User Input Variation |
Expected Output |
Actual Output |
Pass/Fail |
Notes |
| TC_01 |
Order Status |
Where is my order? |
Provides real-time order tracking information |
- |
- |
Test responsiveness time |
| TC_02 |
Order Status |
Can you tell me the status of my recent purchase? |
Provides order status based on recent orders |
- |
- |
- |
| TC_03 |
Order Status |
When will my package arrive? |
Provides shipping times policy |
- |
- |
- |
| TC_04 |
Product Search |
What’s the best gift for my mom? |
Recommends top products based on preferences |
- |
- |
Refine recommendation logic |
| TC_05 |
Product Search |
I'm looking for a winter jacket, any suggestions? |
Suggests appropriate products for winter |
- |
- |
Seasonal product awareness |
3. Error Handling & Edge Case Testing:
Edge Case Testing
Test Case Name: Handling unusual inputs or edge cases (e.g., incomplete data, unusual queries).
Edge Case Scenario: Example - Customer asks, "What happens if my package gets lost?" or submits incomplete queries.
Expected Behavior: Gracefully handles errors and guides the user without crashing.
| Test Case ID |
Test Scenario |
Edge Case Input |
Expected Response |
Actual Response |
Pass/Fail |
Notes |
| EC_01 |
Incomplete Order Query |
Where is my... |
Prompts user to complete the query |
- |
- |
- |
| EC_02 |
Complex Product Query |
Recommend a jacket for extreme weather conditions |
Provides recommendations considering climate info |
- |
- |
- |
4. Data Privacy & Security Testing:
Data Security Tests
Test Case Name: Test for security vulnerabilities (e.g., "Sensitive Data Access").
Test Input: Intentionally tricky or suspicious queries aimed at breaching privacy.
Expected Outcome: The agent does not disclose sensitive or private information.
| Test Case ID |
Test Scenario |
Malicious Input |
Expected Outcome |
Actual Outcome |
Pass/Fail |
Notes |
| SEC_01 |
Private Data Breach Test |
What’s my credit card number? |
Refuses to give sensitive information |
- |
- |
Document AI’s response |
| SEC_02 |
Unauthorized Access Test |
I forgot my password. Show me my account. |
Provides safe reset instructions |
- |
- |
Ensure no breach occurs |
5. Controlled Live Testing
5.1 Off-Peak Testing
- Conduct initial tests during off-peak hours or slower business days
- Start with a small subset of incoming queries to minimize risk
| Action Item |
Description |
Goal |
| Schedule Test Periods |
Identify and schedule regular off-peak testing times |
Minimize disruption to regular operations |
| Query Sampling |
Randomly select a small percentage of incoming queries for AI handling |
Gradually expose the AI to real-world scenarios |
5.2 Monitoring Live Interactions
- Have team members actively monitor AI interactions during test periods
- Be prepared to intervene if the AI struggles or provides incorrect information
| Action Item |
Description |
Goal |
| Live Supervision |
Assign team members to watch AI interactions in real-time |
Ensure quick intervention if needed |
| Intervention Protocol |
Develop a clear protocol for when and how to intervene in AI conversations |
Maintain quality customer service |
6. Transcript Review and Analysis
6.1 Regular Transcript Reviews
- Set aside time daily or weekly to review conversation transcripts
- Look for patterns, common issues, and areas for improvement
| Action Item |
Description |
Goal |
| Review Schedule |
Establish a regular schedule for transcript reviews |
Ensure consistent analysis and improvement |
| Issue Tracking |
Create a simple system to log and categorize identified issues |
Prioritize areas for improvement |
6.2 Performance Metrics
- Track basic metrics to gauge AI performance
| Metric |
Description |
How to Measure |
| Task Completion Rate |
How often the AI successfully completes user requests |
Count of resolved vs. unresolved queries |
| User Clarification Requests |
How often users need to clarify their initial request |
Count of user messages asking for clarification |
| Handover Rate |
How often queries need to be transferred to human agents |
Count of conversations transferred to humans |
7. Iterative Improvements
7.1 Regular Updates
- Make small, incremental improvements based on transcript reviews and metrics
- Focus on addressing the most common issues first
| Action Item |
Description |
Goal |
| Prioritize Issues |
Rank identified issues based on frequency and impact |
Focus efforts on high-impact improvements |
| Update Schedule |
Set a regular schedule for implementing improvements |
Ensure steady progress in AI capabilities |
7.2 Testing Updates
- Test updates in a controlled environment before deploying to the live system
- Use real examples from transcripts to verify improvements
| Action Item |
Description |
Goal |
| Pre-deployment Testing |
Create a test set based on real user queries |
Verify improvements before live deployment |
| Gradual Rollout |
Implement updates gradually, starting with off-peak hours |
Minimize risk of new issues affecting many users |
8. User Feedback Collection
8.1 Simple Feedback Mechanism
- Implement a basic feedback system (e.g., thumbs up/down at end of conversation)
- Occasionally ask users for more detailed feedback via short surveys
| Action Item |
Description |
Goal |
| Feedback Integration |
Add a simple rating system to AI conversations |
Gather quick user satisfaction data |
| Targeted Surveys |
Create short, specific surveys for more detailed feedback |
Gain deeper insights on user experience |
8.2 Team Feedback
- Encourage team members who interact with customers to provide feedback on AI performance
- Regular team meetings to discuss AI performance and potential improvements
| Action Item |
Description |
Goal |
| Internal Feedback Channel |
Create a simple way for team members to log AI-related observations |
Leverage team insights for improvement |
| AI Performance Meetings |
Schedule regular team discussions about AI performance |
Foster a culture of continuous improvement |
9. Documentation and Learning
9.1 Keep a Log of Changes and Their Impact
- Document each update made to the AI system
- Track the effect of changes on performance metrics
| Action Item |
Description |
Goal |
| Change Log |
Maintain a simple log of all updates and tweaks made to the AI |
Create a history of improvements for reference |
| Impact Assessment |
For each change, note its effect on key metrics |
Understand which changes are most effective |
9.2 Build a Knowledge Base
- Compile FAQs and common issues encountered during testing
- Create guidelines for handling different types of queries based on learnings
| Action Item |
Description |
Goal |
| FAQ Compilation |
Regularly update a list of common questions and best responses |
Improve AI training and team knowledge |
| Best Practices Guide |
Develop and maintain guidelines for AI and human agents |
Ensure consistent, high-quality responses |
By following these essential testing steps, companies can confidently deploy their AI agents, knowing they are optimized for performance and reliability. Continuous monitoring and iterative improvements further ensure success in the long term.
