Manual vs Automated Testing for AI Agents: Which Approach Works Best?

As more businesses experiment with building AI agents, the need to ensure their quality grows daily. AI testing is unique, requiring additional knowledge and skills specific to this domain.

This article explores the testing approaches that will ensure AI agents work effectively. We will also discuss the best ways to combine testing methods for optimal results. Whether you’re new to AI or a seasoned expert, this guide will help you understand how to test AI effectively.

Testing Needs of AI Agents

AI agents are software programs that can think and act like humans. They understand language, recognize images, make decisions, and learn from experience. You see them in many places:

• Chatbots: These agents can answer your questions on websites or in apps.
• Virtual Assistants: Agents like Siri, Alexa, and Google Assistant can help you with tasks.
• Recommendation Systems: Agents suggest what products to buy or what movies to watch.
• Autonomous Vehicles: Self-driving cars use AI agents to navigate roads.

As AI agents become increasingly common and powerful, it is crucial to test them thoroughly. Traditional testing may not be sufficient for AI agents, and they require a special approach. AI needs to comply with industry standards, which can be difficult due to their unique qualities:

• Learning and Adapting: AI agents can change their behavior as they learn from new data. Frequent testing is needed to ensure they continue to work correctly.
• Making Decisions: AI agents often make decisions that affect people’s lives. We must test these decisions to check if they are fair, accurate, and unbiased.
• Understanding Language: Some AI agents use natural language processing (NLP) to understand human language, so we need to test how well they understand different accents, slang, and grammar.
• Reasoning and Logic: AI agents use complex logic. In this case, we should test that the logic and reasoning are correct.

In short, we must thoroughly test AI agents to ensure their reliability, safety, and performance. This will help businesses and users not only trust them, but also be able to depend on them.

Manual Testing for AI Agents

Manual testing means having real people check the AI agent to see how it works. These testers interact directly with the AI agent, trying different scenarios and looking for potential issues. Even with all the new tools for automated testing, manual testing remains crucial for AI agents. Here’s why:

• Understanding the User Experience. AI agents are often designed to interact with people. Manual testing lets us see how easy and natural these interactions feel. Human testers can judge things like:
  • Is the agent easy to talk to?
  • Does it understand what the user wants?
  • Is the interaction smooth and helpful?
• Finding Unexpected Problems. AI agents can sometimes exhibit unexpected behavior, especially when confronted with new or unusual situations. Human testers are very good at finding these kinds of “surprises” because they can:
  • Try things that automated tests might miss.
  • Use their intuition and creativity to explore the agent’s behavior.
  • Identify problems that are hard to define with strict rules.
• Evaluating Subjective Qualities. Test software makes some of an AI agent’s most essential qualities difficult to measure. For example, is the agent polite, friendly, or empathetic? Human testers can give us valuable feedback on these subjective aspects. In short, manual testing provides a human touch that automated testing can’t replace.

Automated Testing for AI Agents

Automated testing uses specialized software tools and scripts to execute predefined test cases automatically. Instead of manual testers performing steps, entering data, and verifying results, automation tools perform these actions to compare expected and actual outcomes. This is becoming increasingly important as AI agents grow more complex and require frequent testing. Here are some key benefits of automated testing for AI agents:

• Speed and Efficiency. Automated tests run much faster than manual tests. They can also run 24/7, allowing for more frequent testing.
• Consistency and Reliability. Automated tests do the same thing every time, helping avoid mistakes and ensuring reliability.
• Scale and Coverage. Automated testing can be easily scaled up to test numerous scenarios, covering a broader range of the AI agent’s functionality. This is especially important for complex AI agents with many features.
• Early Detection of Problems. Automated tests can be run whenever changes are made to the AI agent, allowing for early detection of potential issues. Automated tests enable developers to identify and resolve issues before they become large problems.
• Cost-Effectiveness. While setting up automated testing may incur an initial cost, it can save money in the long run by reducing the need for manual testing and identifying problems earlier.

In summary, automated testing is a powerful method for ensuring that AI agents are thoroughly and efficiently tested. It provides speed, scale, and consistency, essential for modern software development.

Key Testing Concepts for AI Agents

Understanding fundamental software testing principles is essential for testing AI agents effectively. These concepts help us plan our tests, measure the agent’s effectiveness, and ensure we have tested them thoroughly.

1. Test Cases. A test case is a specific set of actions that we perform to check if the AI agent works correctly. Each test case has:
   • A description of what we’re going to do
   • The steps we’ll follow
   • What we expect the AI agent to do

   For example, a test case for a chatbot might be: “The user asks ‘What’s the weather like today?’ Check if the chatbot gives the correct weather for the user’s location.”

2. Test Coverage. This indicates the extent to which the tests have exercised different parts or aspects of the AI agent. It’s a way to measure the completeness of our testing. Good test coverage (about 80%) means we have tested many parts of the agent across various situations. There are different ways to measure test coverage, such as:
   • Have we tested all the main features of the agent?
   • Have we tested the agent with different types of inputs?
   • Have we tested the agent in different environments?
3. Test Data. AI agents learn from data, making the test data utilized in their testing a crucial component. We must use various test data sets to ensure the agent works well in different situations. This test data should include:
   • Correct data
   • Incorrect data
   • Edge cases (unusual or rare situations)
   • Data that represents real-world use
4. Test Results and Test Outcomes. After running our tests, we must carefully review the test results:
   • Did the AI agent perform as expected?
   • Did it give the correct answer?
   • Did it make the right decision?
5. Test Failures. When the AI agent fails to perform as expected, we refer to it as a test failure. It is crucial to track these test failures, address the issues, and then re-test to ensure the problems are resolved. By understanding these key concepts, we can design more effective tests and better understand how well our AI agents are performing.

Comparison of Testing Approaches

Manual and automated testing play crucial roles in ensuring AI agents function correctly. Each testing approach has its strengths and weaknesses. Manual testing offers the insight and flexibility of a QA engineer, while automated testing offers speed, consistency, and scale. Choosing the right testing approach (or combination of methods) depends on the AI agent’s specific needs. Here’s a comparison of these two approaches:

Manual vs. Automated Testing for AI Agents

In many cases, the most effective testing strategy is to combine manual testing and automated testing. They can work together to provide a more complete and practical evaluation of the AI agent.

Specific Testing Techniques for AI Agents

Because AI agents differ from regular software, we need to employ specialized tools and testing techniques. These techniques help us assess AI agents’ unique capabilities in learning, decision-making, and language understanding. Here are some critical testing techniques for AI agents:

• Regression Testing. AI agents can evolve as they learn, so it’s essential to ensure that new changes do not break existing features. Regression testing involves re-running tests that have been run before to verify that everything still works as expected. This process is part of test maintenance, as we need to keep these tests up to date.
• Performance and Scalability Testing. We need to check how well AI agents perform under different conditions:
  • Performance testing checks how fast the agent responds and how much it can handle.
  • Scalability testing checks whether the agent can handle increased users or data without slowing down or crashing.
• UI and API Testing. AI agents often interact with users through a user interface (UI) or other software through an application programming interface (API).
  • UI testing checks if the user interface is easy to use and understand.
  • API testing checks if the agent communicates correctly with other software.
• Unit Testing. This technique involves testing small parts of the AI agent in isolation to ensure each component works correctly.
• User Acceptance Testing. This is the final testing stage, where the AI agent is tested in a real-world scenario to ensure it meets the user’s needs.
• System Testing. This involves testing the entire AI system, including all its components and integrations.
• Testing Natural Language Processing (NLP) Agents. For AI agents that understand and use language, we need to test some specific things:
  • Can the agent understand different accents and dialects?
  • Can it handle slang, grammatical errors, and unusual sentence structures?
  • Does it know the meaning behind the words, not just the words themselves?

We can better understand our AI agents’ performance and identify potential issues faster by using combinations of impactful, specified testing techniques.

Our AI Agent Testing Expertise

As AI agents become more integrated into our lives and businesses, a balanced testing approach combining manual and automated testing’s strengths becomes essential. Emphasizing continuous testing, prioritizing test coverage, and using realistic test data are key to ensuring AI’s reliability, safety, and ethical behavior. These very principles guided our quality assurance for Evolv, an AI-powered UX optimization platform.

At QAwerk, we understand the unique challenges of testing AI agents. Since 2015, we’ve tested over 300 products. To demonstrate the value we can offer through our meticulous approach, we invite you to request a free exploratory testing round, joining other innovative AI startups that quickly resolved critical issues we brought to light. Here are some examples of the critical bugs we’ve helped them address:

• FYI.AI: After a user refreshes the “Search” field on the chats page, no chats are displayed.
• VisualMind: The app crashes when a user attempts to copy the email address from the Help Center.
• Dopple.AI: Users can successfully log back into accounts that have been previously deleted.
• Humango: AI Training Planner: The home page becomes blocked (requiring a re-login) after users change cards in the “Add New Card” feature.
• Knowt – AI Flashcards & Notes: Users are unable to log-in to the app using their Google account due to an endless loading spinner.

Need to improve your AI product’s quality? Contact us today for a free consultation to discuss your QA needs and how we can help improve your testing workflows.

FAQ

What is the difference between AI testing agents and testing AI agents?

AI Testing Agents and Testing AI Agents sound similar, but have distinct meanings. AI Testing Agents are AI-powered software tools designed to test other software. They use AI techniques to automate, enhance, and optimize the testing process for applications, systems, or software.

Testing AI Agents refers to evaluating the quality, reliability, and safety of AI-based systems or agents. It verifies that the AI system functions as intended and meets specific performance criteria.

How can AI testing agents improve software testing?

AI testing agents enhance software testing by:

• Automating repetitive tasks frees human testers
• Increasing test coverage through diverse scenarios
• Enabling early defect detection via continuous monitoring
• Improving accuracy and consistency, reducing human error
• Providing faster feedback for quicker issue resolution
• Prioritizing critical test cases intelligently
• Reducing maintenance with self-healing tests

Which is better, manual testing or automation testing?

The ideal approach depends on the specific testing goals as well as the AI agent. Manual testing evaluates user experience, explores unexpected scenarios, and judges subjective qualities. Automation testing is more efficient for repetitive tasks, regression testing, and large-scale testing. A strong testing strategy often utilizes a blend of both approaches.

In which cases is automated testing of AI agents more appropriate?

Automated testing is most preferred for test cases that are:

• Repetitive: Cases that must be run often, such as regression testing.
• Time-consuming: Cases that would take considerable time to run manually.
• Require high consistency: Cases where it’s crucial to get the same results every time.
• Large-scale: Cases where many variations or combinations need to be tested.
• Performance-related: Cases that measure speed, load capacity, and stability.