10 Reasons Why Manual API Testing Breaks Down in the AI Era

The Illusion of "Human Verification is Enough"

This happened to my team last month.

I was generating API code with ChatGPT and testing it manually as usual. "Even if AI writes the code, as long as humans check it, it should be fine," I thought.

But then, one day, manual testing completely broke down.

The API changes were happening too fast for testing to keep up. Before I knew it, APIs that I thought I had verified were causing errors in production one after another.

"Why did this suddenly stop working when it was fine before?"

When I found the answer, I realized that the problem wasn't my skills, but that the structure of the era itself had changed.

This article shares 10 lessons I learned from experiencing the breakdown of manual API testing.

If you also think "manual testing is still fine," this story might not be someone else's problem.

The Moment I Experienced the Breakdown

1. Humans Can't Keep Up with AI Code Generation Speed

This was the biggest shock.

When I ask ChatGPT to "create a user management API," it generates code for 10 endpoints in 5 minutes. But trying to test that manually takes more than a day.

• Endpoints increase by 5 every 30 minutes
• Request structures change 3 times in an hour
• Just confirming the impact scope takes half a day

I thought "Why am I so slow?" but the problem wasn't my skills, it was the structure itself.

2. Falling into the "Trap" of AI-Generated Code

AI-generated APIs look incredibly polished at first glance.

// API generated by ChatGPT (looks perfect at first)
app.post('/api/users', async (req, res) => {
  const { name, email } = req.body;
  const user = await User.create({ name, email });
  res.json({ success: true, user });
});

"Wow, it works! Perfect!" I thought, testing only the happy path manually and calling it done.

But I later discovered:

• Empty string handling is missing
• No duplicate email check
• Error response formats are inconsistent

With manual testing, you end at "it works, so it's OK". Edge cases and error scenarios get pushed aside because they're tedious.

3. Test Case Update Hell

This was also painful.

When you change API specifications rapidly with AI, test case updates can't keep up.

• Monday: Specification updated
• Tuesday: Implementation changed
• Wednesday: "Wait, what about the test cases?"
• Thursday: "I don't know what we're testing anymore"

As a result, you end up in the absurd situation of testing new APIs with old test cases.

4. Test Results Vary by Person

This was the most frustrating in team development.

• Me: "This API works fine"
• Colleague A: "I'm getting errors"
• Colleague B: "The response is slow"

The same API produces different results depending on who tests it.

Even with documentation:

• Environment differences
• Different checkpoints
• Different ways of creating test data

cause results to vary. This doesn't ensure quality.

Analyzing the Root Causes of the Breakdown

5. Test Results "Disappear"

Where do manual test results go?

• My local Postman environment
• Slack messages saying "It works!"
• Handwritten notes

They're all temporary. The next day, it's "Wait, what did I test yesterday?"

As AI speeds up development, this "disappearing" problem becomes critical.

6. Completely Left Out of CI/CD

This also hurt.

While the team runs CI/CD with GitHub Actions, manual testing is completely outside the loop.

• Code auto-deploys on push
• But API testing is manual
• Result: "Problems discovered after deployment"

Testing became "something to do if there's time at the end".

7. Multi-API Integration Testing Isn't Realistic

APIs created by AI often assume multiple APIs working together, not standalone.

• User registration → Authentication → Profile update
• Product search → Cart addition → Payment processing

Testing this manually every time is not realistic.

8. "Test Design" Becomes Personal Knowledge

This was the scariest.

AI writes code → Humans design tests → Only specific people understand the whole picture

If I'm absent, no one can test the APIs anymore.

The Problem of Fragmented Development Flow

9. Specifications, Tests, and Implementation Are Scattered

I think this was the biggest cause of the breakdown.

• Specifications: Notion
• Tests: Postman
• Implementation: AI-generated code

They're all in different places, so consistency is impossible.

When specs change, test case updates are forgotten. When implementation changes, spec updates are forgotten.

To resolve this fragmentation, we eventually migrated to tools that can manage API specifications and tests in the same place (like Apidog).

Actually, after starting to use Apidog:

• Specification changes → Test cases auto-update
• Test execution → Results remain as history
• CI/CD integration → Automated tests run

This flow emerged, and the situation of "not knowing what or where we're checking" decreased significantly.

10. The Terror of "Thinking We've Verified"

The most dangerous thing was the false sense of security.

"Humans are watching, so it's fine" / "I touched it and it worked, so there's no problem"

But in reality, it had become impossible for humans to see everything.

Manual testing was meant to be "reassurance" but actually became a "blind spot".

I'm Not Completely Rejecting Manual Testing

I don't want to be misunderstood—manual testing itself isn't bad.

Even now, manual testing is important for:

• Initial verification of new features
• UI integration checks
• Usability checks

However, the structure of keeping manual testing as the "main" approach breaks down.

The Solution I Found: Unified Foundation

Ultimately, it was important to manage the following on the same foundation:

• API specifications
• Test cases
• Mock data
• Execution results

Recently, approaches that manage these centered around API specifications are becoming more common.

For example, using tools that can handle API definitions and tests on the same foundation (like Apidog) can help detect discrepancies with AI-generated code earlier.

※This is just one example; what's important is having "a structure that doesn't break down".

Summary: What Needs to Change is the "Foundation" Itself

In the AI era, what needs to be reconsidered isn't:

• Manual vs automated
• Which tool to use

It's the foundation itself that "humans can verify everything".

I also had resistance at first. But when I let go of that foundation, the way I approach API testing naturally changed.

If you're also feeling the "limits of manual testing," please take a moment to think about it.

The problem might not be your individual skills, but the result of the era's structure itself changing.