Stop Asking AI to Fix the Bug: Why Kiro's Bugfix Specs Take a Better Approach
Moving from ad-hoc bug fixing to structured root cause analysis in enterprise software development
Introduction
One of the most common ways developers use AI today is simple.
We copy an exception, paste it into the chat, and ask
"Can you fix this bug?"
Sometimes it works.
Sometimes it even produces a working solution.
But anyone who has worked on a large enterprise application knows the real challenge isn't making the error disappear.
The challenge is fixing the problem without introducing another one.
In production systems, a bug fix is rarely just about changing a few lines of code. We need to understand why the issue occurred, identify the smallest possible change, and verify that existing functionality continues to work as expected.
That's exactly the problem Kiro's new Bugfix Specs are designed to solve. Rather than jumping straight to implementation, they guide developers through a structured workflow focused on root cause analysis, fix design, and regression prevention.
Why AI Bug Fixes Often Go Wrong
Imagine a production issue is reported:
Orders API intermittently returns HTTP 500 when processing international addresses.
A typical AI prompt might be:
"Fix this bug."
The model analyses the code and proposes a solution.
The application compiles.
The failing scenario is resolved.
But a few days later, another issue appears.
Domestic addresses no longer validate correctly.
The original bug is gone.
A regression has been introduced.
Most of us have experienced something similar.
The problem wasn't the AI.
The problem was the lack of context about what should remain unchanged.
How Experienced Developers Usually Fix Bugs
When senior engineers investigate production issues, they rarely start by writing code.
They usually ask questions like:
- Under what conditions does the issue occur?
- What is the actual root cause?
- Which components are affected?
- Which behaviour must remain unchanged?
- How can we prove the fix doesn't introduce regressions?
Only after answering those questions do they start implementing the solution.
Kiro's Bugfix Specs follow a very similar thought process.
The Bugfix Workflow
Instead of immediately generating code, Kiro structures the investigation into three stages.
Rather than asking the AI to "fix the bug," you're guiding it through the same process an experienced engineer would typically follow.
Capturing What Changes and What Doesn't
One part of the documentation stood out to me.
During the analysis phase, Kiro doesn't only ask what is broken.
It also captures:
- Current Behaviour – what the system does today.
- Expected Behaviour – what should happen after the fix.
- Unchanged Behaviour – what must continue working exactly as before.
That third category is particularly important.
When fixing production issues, preserving existing behaviour is often just as important as correcting the defect itself.
Example - Login Issue
Imagine a banking application where users report they can't log in after resetting their password.
Instead of simply asking Kiro
"Fix the login bug."
A Bugfix Spec captures the problem more precisely.
Current Behaviour
- Users who reset their password receive "Invalid Credentials" when attempting to log in.
Expected Behaviour
- Users should be able to log in successfully after resetting their password.
Unchanged Behaviour
- Existing users who have not reset their password should continue to log in without any changes.
- Multi-factor authentication (MFA) should continue to work as before.
This gives Kiro much more context. It not only understands what needs to be fixed, but also what must not be affected by the fix.
Example 2 – Payment Processing
Imagine an e-commerce application. Customers report that credit card payments are failing.
A Bugfix Spec could describe the issue like this
Current Behaviour
- Visa card payments fail with a 500 Internal Server Error.
Expected Behaviour
- Visa payments should complete successfully.
Unchanged Behaviour
- Mastercard and UPI payments should continue to work exactly as before.
- Existing fraud detection rules should remain unchanged.
Now the AI understands the scope of the fix instead of modifying unrelated payment logic.Designing the Fix Before Writing Code
After understanding the bug, Kiro analyses the repository and generates a design describing:
- the likely root cause
- the proposed implementation approach
- the properties that should be validated
- the regression scenarios that need testing
This shifts the focus from writing code quickly to designing a reliable fix.
Regression Prevention Is Built Into the Workflow
Another capability I found interesting is how Bugfix Specs approach testing.
Instead of validating only that the bug is fixed, Kiro generates tests to verify three things:
- the bug can be reproduced,
- the proposed fix resolves it, and
- previously working behaviour continues to work.
That aligns closely with how many enterprise teams think about production fixes.
A successful bug fix isn't simply one that removes an error.
It's one that avoids creating new problems.
When Would I Use Bugfix Specs?
Based on the documentation, I think Bugfix Specs are particularly useful for:
- production issues where regressions are expensive,
- bugs in critical business workflows,
- complex defects requiring root cause analysis,
- teams that need a documented record of why a fix was implemented,
- applications where preserving existing behaviour is as important as introducing the fix.
For small UI tweaks or trivial issues, a normal prompt is probably sufficient.
But for production systems handling business-critical workloads, I can see the structured workflow being valuable.
Final Thoughts
One thing I appreciate about Bugfix Specs is that they don't try to replace the way experienced engineers think.
Instead, they formalise it.
Rather than asking AI to jump straight into code, the workflow encourages understanding the problem first, designing the fix carefully, and explicitly protecting existing behaviour before implementation begins.
For enterprise software, where the cost of regressions is often much higher than the cost of the original bug, that feels like a practical improvement.
In many cases, the biggest challenge isn't fixing the bug—it's fixing it without breaking everything that already works. I think Bugfix Specs are an interesting step towards helping AI-assisted development solve that problem more reliably.
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