When AI Agents Test Software, Who Checks the Tester?

AI is becoming capable of doing more than generating test cases. AI agents can help plan test steps, interact with applications, execute workflows, and analyze failures.

That sounds promising. But while exploring AI-assisted testing, I kept coming back to one question:

If AI can test an application, how do we know it tested the right thing?

Imagine an AI agent testing an online shopping website. It selects a product, clicks Checkout, and completes the payment flow. The test reports a pass.

But was the payment actually successful? Was the correct order created, and was the right amount charged?

That's where QA comes in. The QA engineer needs to verify whether the AI tested the correct business rules, checked the expected results, and missed any important scenarios.

The test passing is one thing. Knowing that it tested the right thing is another.

The test executed successfully, but did it really verify that the application worked correctly?

That's the difference I've been thinking about. A test passing doesn't automatically mean the right behaviour was tested.

This becomes more important as AI agents take on more testing responsibilities. Generating a test, executing it, and evaluating the result are different steps. We still need to understand whether the test matches the original requirement, whether its assertions are meaningful, and whether important scenarios have been covered.

While exploring different approaches to AI-assisted testing, I found it useful to look beyond test generation alone. The wider workflow matters too: understanding requirements, identifying coverage, executing tests, maintaining them when the application changes, and investigating failures with useful evidence.

This is what makes me think about the broader testing workflow at X360 AI Tech. The focus connects requirements, test coverage, automation, self-healing, and failure analysis rather than treating each as an isolated activity.

For example, execution evidence such as logs, screenshots, videos, and network activity can help QA understand what happened during a test instead of relying only on a pass-or-fail status. That context can make investigation easier, although QA still needs to verify whether the test and its conclusions are correct.

I don't think the goal should be to remove QA from the process. It should be to reduce repetitive effort while giving QA better context to make informed decisions.

As AI agents become more involved in software testing, I think checking the quality of their work will become just as important as measuring how much they can automate.

I'm curious to know if an AI agent creates, executes, and analyses a test, what would you want to verify before trusting its result?