Point AI at Your Bug Backlog First

Everyone’s plugging AI into the wrong end of the backlog

Here’s my problem with how teams are onboarding AI coding assistants: they all start with new features. Shiny stuff. The demo everyone wants to see. And it’s backwards.

The best first place to point an AI assistant is your bug backlog. Not because bugs are glamorous, but because a well-qualified bug is the most precisely specified work your team owns. A reproduction case. An expected result. An actual result. And the source code sitting right there. The validation loop is closed before you even start. You know in minutes whether the fix holds.

AI shines exactly where the outcome is verifiable fast. And nothing is more verifiable than a bug with a solid repro.

The perfect spec already exists, and it’s a bug ticket

We keep hunting for the ideal prompt, the ideal spec, the perfect way to describe intent to a machine. Meanwhile the best spec your team writes all week is a good defect ticket.

Think about what a proper bug report actually contains. The full contract. How to reproduce it, what should happen, what happens instead. No user story ever gets close to that level of precision. A story says “as a rider I want to see my next departure.” A bug says “on iOS 17, tap refresh twice within one second, the list duplicates, here’s the stack trace, here’s the line.”

One of those is a wish. The other is a contract.

So if the whole game with AI is minimizing ambiguity, why do we hand it the work where ambiguity is maximal and keep the tightly specified work for ourselves? Give the tool the job where the target is nailed down.

Pick the terrain by the length of the loop, not the prestige of the task

This is the part people get emotionally wrong. Features feel like the noble work. Bugs feel like the chore. So the instinct is to put your fastest new tool on the most impressive task and keep humans grinding on the chore. Do the opposite.

The criterion that matters isn’t novelty. It’s how long the validation loop is.

1. On bounded work you can verify in minutes, the AI gain is massive. Generate, run the repro, done or not done.
2. On open-ended work, integration, data migrations, content, the gain collapses. The time doesn’t disappear, it just moves into verification, and verification of ambiguous work is slow and human.

A bug with a repro is the shortest loop you have. That’s the whole reason to start there. Not because bugs are important. Because they’re checkable.

The real quality risk isn’t wrong code, it’s plausible code

Let me be blunt about the danger, because most governance conversations aim at the wrong one. The problem with AI-generated code isn’t that it produces something obviously broken. Obviously broken is easy. You catch it and move on.

The problem is code that’s plausible and wrong. It reads clean. It compiles. It looks like something a decent developer would write. And it’s quietly incorrect. That’s the code that ships and bites you three sprints later.

A bug that comes with a reproduction test makes plausible-but-wrong detectable instantly. Run the test. Green or red. The guardrail exists before the code is generated.

Now look at a brand-new feature. The test that would validate the output doesn’t exist yet. So who writes it? The AI does. The same tool generating the code also generates the check on that code. That’s the fox auditing the henhouse. On bugs, the henhouse came with a lock, and a human put it there first.

What it demands back from you: qualification discipline

This approach isn’t free. It forces a standard. A ticket with no repro and no expected-versus-actual is not ready for the AI.

Here’s the uncomfortable truth: it was never ready for a human either. The human just compensated in silence. A developer picked up the vague ticket, guessed at intent, hunted down the repro themselves, and filled the gaps in their head. All that hidden work was invisible because it happened between the ears of someone senior.

The AI won’t compensate quietly. It’ll confidently fix the wrong thing. So you have to qualify properly. And that’s the virtuous side effect: your backlog quality goes up, because qualification suddenly pays off immediately. When good tickets get resolved fast and bad tickets go nowhere, people start writing good tickets. The incentive finally points the right way.

Human review stays mandatory, and it stays short

None of this removes the human. Pull request, quality gates, and an AI label on the change for auditability, all non-negotiable. If you can’t trace which code came from a machine, you’ve lost the plot on governance.

But here’s the thing about reviewing a bounded fix backed by a repro test: it’s a short review. Narrow scope, clear pass condition. You’re not reasoning about architecture. You’re confirming the fix does what the test says and doesn’t break the neighbors.

And short review is the whole point, because the bottleneck of 2026 isn’t code generation. It’s review drowning under the volume of generated code. Point AI at open-ended features and every pull request becomes a sprawling thing nobody wants to review, so it gets rubber-stamped, and now the plausible-but-wrong code is loose in your system. Start with bugs and each perimeter is narrow by construction. You keep the bottleneck under control precisely because you never let the scope explode.

For the leader: start where you can measure

If you run delivery, this is also the cleanest experiment you’ll ever get to run.

A bug backlog gives you a real before-and-after. Time per fix. Rejection rate at review. Reopen rate. Numbers you already track or can start tracking tomorrow. No hand-waving about “productivity,” no vibes. A clean baseline and a clean delta.

It’s also your strongest argument in front of cautious governance, and in the public sector cautious governance is the whole room. You walk in with a bounded scope, measurable outcomes, low ambiguity, and a safety net that exists before the tool touches anything. That’s a proposal a risk-averse steering committee can actually say yes to. Try selling them “let the AI write our new payment feature” and watch how fast the meeting ends.

Crystal-clear objective: cut the cost of resolving qualified defects. Constraints everyone knows: quality gates hold, every AI change is labeled and reviewed, no scope creep past the ticket. Autonomy on the how, the team picks the tooling and the workflow. Accountability on the result, the metrics tell you if it worked.

Start with the chore. It’s the best-specified work you have, and the only terrain where the safety net comes before the tool.