DEV Community: Robin

Can AI Fix a Bug It Cannot Reproduce?

Robin — Mon, 07 Sep 2026 13:54:33 +0000

A generated patch can be correct without being proven correct. That's the problem.

AI coding agents are getting better at reading code, analyzing errors, and proposing fixes.

But debugging production software has a requirement that code generation doesn't:

You need to establish what actually failed.

A useful way to think about debugging is:

Signal
  ↓
Evidence
  ↓
Hypothesis
  ↓
Reproduction
  ↓
Fix
  ↓
Validation

The important step is in the middle:

Reproduction.

Why Reproduction Matters

Imagine you have this situation:

Customer reports:
"Checkout failed."

Logs:
Payment request returned an unexpected response.

AI analysis:
"Likely caused by an unhandled edge case in payment retry logic."

AI generates:
A patch to the retry condition.

The patch looks reasonable.

But what do we know?

We know the AI found a plausible explanation.

We don't necessarily know that the explanation is correct.

The stronger workflow is:

Original code
      ↓
Reproduce the reported failure
      ↓
Confirm the failure
      ↓
Apply the fix
      ↓
Run the same reproduction
      ↓
Confirm the failure is gone
      ↓
Run regression checks

Now we're testing something concrete.

A Root Cause Is Still a Hypothesis

This distinction is easy to miss.

An AI can say:

"This function is causing the issue."

That is a hypothesis.

If we can reproduce the failure and demonstrate that changing that function removes the failure, we have much stronger evidence.

That's why:

A fix without reproduction is a hypothesis.

What If the Bug Can't Be Reproduced?

Production bugs aren't always convenient.

You may have:

Missing logs
Expired traces
Changed environments
Different database state
Customer-specific data
Race conditions
Timing-dependent failures
Distributed-system state that no longer exists

In these cases, AI may still be able to identify a likely cause.

But the system should distinguish between:

Likely cause

and

Validated cause

Those are not equivalent.

Don't Optimize Only for Patch Generation

If we're going to evaluate AI debugging systems, measuring "did it generate a patch?" isn't enough.

We should measure things like:

Metric	Question
Root Cause Accuracy	Did it identify the real cause?
Reproduction Rate	Could it recreate the failure?
Fix Accuracy	Did the patch solve it?
Edit Precision	How much unrelated code changed?
Regression Rate	Did the patch break something else?
Validation Quality	Is there before/after evidence?
Human Intervention	How much help was required?
Time to Resolution	How quickly did it reach a validated result?

This gives us a more complete picture of debugging performance.

Passing Tests Can Still Mislead You

One more important point.

Imagine an AI writes a test and the test passes.

That's good.

But does it prove the original bug is fixed?

Only if the test actually captures the original failure.

A stronger validation sequence is:

Old code
   ↓
Original failure reproduced
   ↓
Fix applied
   ↓
Same failure no longer occurs
   ↓
Regression tests pass

That gives us an evidence chain.

Maybe "I Don't Know Yet" Is a Good Answer

There's an interesting implication here.

A good AI debugger shouldn't necessarily modify code every time.

If the evidence is insufficient, a useful response might be:

"I don't have enough evidence to safely make this change."

That's not an inability to debug.

It's an acknowledgement of uncertainty.

For production systems, that can be more valuable than confidently editing the wrong code.

The FixBugs Perspective

FixBugs is built around an evidence-first debugging philosophy:

Root Cause → Reproduction → Fix → Validation

The goal isn't simply to generate a patch.

It's to help establish why the bug happened, reproduce the failure, apply the fix, and validate the result.

That's a different standard from:

"The AI changed the code and the tests are green."

The real question is:

"Do we have enough evidence to trust the fix?"

Final Thought

As AI writes more software, the ability to verify software becomes more important.

So maybe the next generation of AI debugging shouldn't be judged by how many patches it can generate.

Maybe it should be judged by how many validated fixes it can produce.

Can AI fix a bug it cannot reproduce?

That's something worth testing.

Coding Is Forward. Debugging Is Backward.

Robin — Tue, 01 Sep 2026 15:38:42 +0000

Coding starts with an intention. Debugging starts with a failure.

Most AI coding workflows follow a forward loop:

Idea → Code → Test → Ship

Production debugging starts from the opposite direction:

Failure → Evidence → Hypothesis → Reproduction → Fix → Validation

That difference matters.

Coding Starts With a Known Outcome

When you ask an AI coding agent to build something, the goal is usually clear.

You know what the system should do.

The agent can:

inspect the repository
understand the requirements
write code
run tests
iterate

The workflow moves toward an intended result.

Debugging doesn't have that luxury.

Debugging Starts With a Symptom

Consider:

500 errors increased after the latest deployment.

That's a signal.

It doesn't tell you the root cause.

You still need to determine:

What changed?
Which requests fail?
Can the failure be reproduced?
Which component is responsible?
Is the visible exception the actual cause?
Does the failure disappear after the fix?

A stack trace tells you where execution failed.

It doesn't automatically tell you why.

Where it failed ≠ why it failed.

Reproduction Is the Critical Step

Suppose an AI says:

The root cause is X.

The next question should be:

Can you reproduce it?

If the current code fails under the same conditions, you have evidence.

If the same reproduction passes after the fix, you have stronger evidence that the fix actually addressed the problem.

The chain becomes:

Root Cause
    ↓
Reproduction
    ↓
Fix
    ↓
Validation

Without reproduction, the root cause is still a hypothesis.

Green Tests Aren't Automatically Proof

An AI can change several files and make the test suite pass.

That doesn't necessarily mean the production bug is fixed.

The tests may not reproduce the original failure.

The test may not cover the affected behavior.

The patch may fix a symptom rather than the cause.

Or the change may introduce a regression.

The better question isn't:

Did the tests pass?

It's:

Did the same failure disappear after the fix?

An Evidence-First Debugging Loop

A stronger AI debugging workflow looks like this:

Signal
  ↓
Evidence
  ↓
Hypothesis
  ↓
Reproduction
  ↓
Fix
  ↓
Validation

Signal

An alert, exception, customer report, timeout, crash, or test failure.

Evidence

Logs, traces, metrics, recent changes, repository state, runtime information, issue history, and environment details.

Hypothesis

A possible explanation.

Not a conclusion.

Reproduction

Recreate the original failure.

Fix

Make the smallest appropriate change.

Validation

Run the reproduction again and check for regressions.

AI Coding Agents vs AI Debugging Agents

A coding workflow can look like:

Create → Execute → Iterate

A debugging workflow needs to look more like:

Investigate → Reproduce → Explain → Fix → Validate

The same model can potentially perform both jobs.

But the workflow and success criteria are different.

A plausible patch isn't the same thing as a proven fix.

The Standard Should Be Higher

The future of AI debugging shouldn't simply be:

AI generated a patch.

It should be:

AI investigated the failure, reproduced it, identified the root cause, fixed it, and validated that the original failure disappeared.

That's the difference between generating a fix and proving one.

Root Cause → Reproduction → Fix → Validation

What should an AI debugger prove before changing production code?

The AI Found the Root Cause. Now What?

Robin — Wed, 26 Aug 2026 16:48:22 +0000

Your AI debugging agent says it found the root cause.

It analyzed the logs, traces, deployments, and code changes. It explains what went wrong and why.

The investigation appears to be over.

But there is still an important question:

How do we know it is right?

A convincing explanation is not the same thing as a proven one.

Imagine an AI identifies a recent deployment as the cause of an incident. The timeline matches. The affected code appears in failing traces. The explanation makes sense.

But correlation is not causation.

The deployment may be unrelated. Another dependency may have changed at the same time. The code may be involved without being the original source of the failure.

AI can generate a plausible explanation quickly. That is useful.

But the next step should not automatically be:

Generate the fix.

It should be:

Can we prove this explanation?

Reproduction Is the Next Step

If the AI believes it understands what caused the failure, we should try to reproduce the conditions it describes.

Can the suspected input, dependency behavior, timing issue, or system state trigger the same failure?

A reproduction does not need to recreate all of production. It needs to capture the conditions that matter.

Then we have something testable.

Does the existing code fail?

If it does, the hypothesis becomes much stronger.

If it does not, that is useful too. The reproduction may be incomplete, or the explanation may be wrong.

Either way, we have learned more than we would by simply trusting the first answer.

The Fix Needs Evidence Too

Suppose the reproduction fails against the existing code.

Now the AI proposes a fix.

The important test is simple:

Before the change, the reproduction fails.

After the change, the same reproduction passes.

That creates a direct connection between the production failure and the proposed solution.

A patch can look correct and still solve the wrong problem.

A passing test can be unrelated to the original incident.

A clean pull request is not proof that the bug is gone.

The strongest evidence is still showing that the demonstrated failure no longer happens after the change.

A Better Standard for AI Debugging

AI debugging should not end when the agent says it found the root cause.

A stronger workflow is:

Signal → Investigation → Root Cause → Reproduction → Fix → Validation

The goal is not just to get an answer.

The goal is to understand what evidence supports that answer and whether the proposed change actually resolves the failure.

At FixBugs, we think AI debugging should help engineers move beyond a plausible explanation and toward evidence they can trust.

The AI found the root cause.

Now prove it.

Why a Coding Agent Is Not the Same as a Debugging Agent

Robin — Fri, 21 Aug 2026 16:05:59 +0000

AI coding agents are getting very good at writing code.

But production debugging does not begin with code.

A coding task usually starts with a known objective:

Build this.

Change this.

Fix this.

A production incident starts differently: > Something is broken.

That means someone has to investigate.

What changed?

Where did the failure start?

Is this the cause or just a symptom?

Which logs, traces, metrics, or code changes actually matter?

A simplified workflow looks like this:


text
Signal
↓
Context
↓
Investigation
↓
Root Cause
↓
Fix
↓
Validation

The Users of Today May Not Be the Users of Tomorrow

Robin — Sat, 15 Aug 2026 06:04:46 +0000

What happens when the customer of your API is no longer a person, but an AI agent?

For years, we've built software around people.

People open the product.

They read the UI.

They compare options.

They decide what to use.

That assumption has influenced how we design software, APIs, dashboards, documentation, and developer tools.

But AI agents are beginning to change how software gets used.

Agents are starting to choose the tools they need, call APIs, read documentation, and get work done with much less human involvement.

And that creates an interesting question for developers:

What happens when the customer of your API is an agent?

An agent doesn't care about a beautiful dashboard.

It cares about whether your API works.

It cares about whether the data is reliable.

It cares about whether it can understand your product quickly.

And it needs to be able to actually use the capabilities you've exposed.

This makes things like APIs, documentation, reliability, and data quality increasingly important.

Observability is an interesting example

Today, observability data is mostly something engineers look at.

An engineer sees an issue.

They investigate.

They look at the available data.

They identify what happened.

And they decide what to do next.

But consider a future where agents consume much more of that data.

Agents could continuously monitor systems.

They could find issues.

They could interpret what they're seeing.

And they could decide what to do next.

That means observability data may increasingly become something consumed by software, not just humans.

And that changes how we should think about the product itself.

The API becomes part of the user experience

If an agent is choosing and using your product, the API isn't just an implementation detail.

It becomes part of the experience.

The agent needs to know:

What does this product do?
What can I call?
How do I call it?
Can I trust the data?
Is the response predictable?
Can I understand the documentation quickly?
Can I actually complete the task using this product?

These questions lead to a broader shift in product thinking.

The old question was: “What do our users want?”

The new question might be: “What does an agent need in order to choose and use our product?”

That could become increasingly important across infrastructure, observability, APIs, and developer tools.

The next generation of products

Maybe the next generation of great products won't just be human-friendly.

They'll be agent-friendly.

Because the users of today may not be the users of tomorrow.

And if agents are increasingly choosing tools, consuming data, and taking action, products will need to work for them too.

Build something agents want.

AI #AIAgents #APIs #DevTools #Observability #Software #DeveloperTools

The Alert Tells You Something Is Wrong. FixBugs Tells You, “I’ll Fix It.

Robin — Mon, 10 Aug 2026 16:50:05 +0000

A production alert fires.

You know something is wrong.

But the alert rarely tells you why.

So the debugging process begins.

Check the logs.

Look at traces.

Review recent deployments.

Search the codebase.

Try to reproduce the issue.

Find the root cause.

Write a fix.

Run tests.

Review the changes.

Open a PR.

That entire process can take minutes.

Or hours.

And sometimes the hardest part isn't writing the fix.

It's figuring out what actually happened.

That's what we're trying to solve with FixBugs AI.

We're building an AI debugging agent designed to work through production issues from the initial alert all the way to a validated fix.

The workflow we're working toward looks like this:

Alert
↓
Investigation
↓
Root Cause
↓
Reproduction
↓
Fix
↓
Validation
↓
Pull Request

The interesting part is that debugging requires much more context than simply looking at a piece of code.

You need to understand what the application was doing.

What changed recently.

What the logs are saying.

What the traces are showing.

Which part of the code is involved.

And whether the proposed change actually fixes the underlying problem.

That's why we're building FixBugs around the debugging workflow itself.

Not just code generation.

Not just autocomplete.

Not just another chatbot.

The goal is to have AI investigate the problem, reason about the available context, reproduce the issue when possible, work on a fix, validate the change, and prepare it for an engineer to review.

The engineer stays in control.

AI handles the investigation and repetitive debugging work.

The engineer decides what ships.

That's the idea.

The alert tells you something is wrong.

FixBugs tells you: "I'll fix it."

If you're a developer or SRE, I'm curious:
What's the most time-consuming part of debugging production issues for you?

https://fixbugs.ai/

Why Building an Internal AI Platform Costs More Than You Think...

Robin — Wed, 05 Aug 2026 16:31:12 +0000

Building Your Own AI Platform? Think Twice Before Building In-House

Every engineering team reaches a point where AI starts showing real business value.

The next question is almost always:

Why don't we build it ourselves?

It sounds reasonable.

But building an AI platform involves far more than connecting to an LLM.

What You're Actually Signing Up For

When you build internally, you also own:

Development costs
Infrastructure
AI model costs
Security
Monitoring
Maintenance
Continuous improvements

A Prototype Isn't the Product

Getting a demo running is relatively easy.

Getting it production-ready requires:

AI Engineers
Backend & Frontend Developers
DevOps
QA
Security
Product Management

Eventually, your internal AI tool becomes another product your engineering organization must support.

The Cost Nobody Talks About

AI infrastructure keeps evolving.

Your team will constantly deal with:

Model updates
API changes
Prompt improvements
Reliability testing
Performance optimization
Scaling
Security patches

The work never really ends.

The Three Permanent Phases

Build
Operate
Improve

Every successful internal AI platform lives inside this cycle forever.

The Real Question

Instead of asking:> Can we build it?

Ask:> Should we build it?

If your goal is helping engineers investigate incidents faster, your best investment may be focusing engineering time on customer-facing products—not AI infrastructure.

That's why Build vs Buy is ultimately an economic decision, not just a technical one.

What would your team choose?

Build or Buy?

About FixBugs

At FixBugs, we help engineering teams accelerate incident investigation with AI without spending months building and maintaining their own AI platform.

Learn more: https://fixbugs.ai/

Stop Measuring AI by Lines of Code. Measure It by Engineering Hours Saved.

Robin — Mon, 03 Aug 2026 12:23:57 +0000

Whenever engineering teams evaluate AI tools, the first question is usually:

"How much faster will developers write code?"

I think that's the wrong question.

A better question is:

How much engineering time are we wasting investigating production incidents?

Because that's where a surprising amount of money quietly disappears.

Let's do a simple calculation.

No complicated ROI formulas.

No marketing buzzwords.

Just numbers.

Imagine Your Engineering Team Looks Like This

Team size: 50 engineers
Production incidents: Around 180 per year
Engineers involved in each incident: 5
Average investigation time per engineer: 2.5 hours
Average engineering cost: $80/hour

Nothing unusual.

In fact, these numbers are fairly common for many growing SaaS companies.

Engineering Hours Lost Every Year

Each production incident requires:

5 engineers × 2.5 hours = 12.5 engineering hours

Across 180 incidents:

180 × 12.5 = 2,250 engineering hours every year

Read that again.

2,250 engineering hours.

That's time spent:

Understanding production failures
Jumping between logs
Looking at dashboards
Checking traces
Reading deployment history

Instead of:

Shipping new features
Improving reliability
Reducing technical debt
Building products customers love

Now Translate Those Hours Into Money

If engineering costs average $80/hour:

2,250 × $80 = $180,000/year

That's $180,000 spent just investigating incidents.

And for many organizations, nobody actually measures this cost.

Maybe We're Measuring AI Incorrectly

Most AI discussions focus on:

Writing code faster
Autocomplete
Pull request generation

Those are valuable.

But once software reaches production, another problem begins.

Finding the root cause.

And that's often where engineering teams lose the most time.

Why We Built FixBugs

This problem is exactly why we're building FixBugs.

Instead of making engineers jump across logs, traces, code, deployments, alerts, and documentation, FixBugs automatically gathers the debugging context and helps teams reach the root cause much faster.

Learn more:

👉 https://fixbugs.ai/

What If Investigation Time Drops By 50%?

Current effort:

2,250 engineering hours/year

With a 50% reduction:

1,125 engineering hours saved

Financial impact:

1,125 × $80 = $90,000/year

More importantly...

Those hours go back to engineering teams.

Not by replacing engineers.

But by helping them spend more time building instead of investigating.

Four Questions Every Engineering Leader Should Ask

Instead of asking:

"Does this AI tool generate code?"

Ask:

How many production incidents do we investigate every year?
How many engineering hours do they consume?
What is one engineering hour worth?
How much of that time could realistically be recovered?

Those answers build a much stronger business case than any marketing claim.

Final Thoughts

AI ROI shouldn't be measured by how many lines of code it generates.

It should be measured by how much engineering time it gives back.

For many organizations, the biggest opportunity isn't writing software faster.

It's spending less time figuring out why production broke.

Sometimes the highest ROI doesn't come from hiring more engineers.

It comes from helping the engineers you already have spend more time building—and less time investigating.

Discussion

How does your engineering team currently investigate production incidents?

Do you actually measure the engineering hours spent on incident investigation?

I'd love to hear how other teams approach this.