A few weeks ago, I wrote about how vibe coding is creating front row seats to the next wave of industry disruption – comparing it to software’s Blockbuster moment. The response was immediate: “but what about code quality?” and “how do you manage the chaos?”

Those questions led me to explore what I call the maturation curve of AI-assisted development, which I detailed in my latest post on spec-driven development with AI coding agents. The reality is that vibe coding gets you lightning-fast prototypes, but the gap between impressive demos and production systems is where most teams struggle.

Here’s what I didn’t fully explore in either post: the mechanism that bridges this gap isn’t just better prompts or smarter AI. It’s fundamentally changing how we approach issue tracking.

Think about your typical development workflow. Business stakeholder says “users want better reporting.” Developer spends an hour in Slack trying to decode what that means. They build something. Three feedback rounds later, it’s still not right. Sound familiar?

Now imagine this: you describe that feature to your coding assistant, and it generates comprehensive Git issues – complete with user stories, acceptance criteria, technical requirements, and test cases. But here’s the key difference: these aren’t just documentation. They become the single source of truth that drives your entire development process.

Good issue tracking creates what I call “shared certainty” across your team. When your Git issue clearly defines “done” before anyone writes code, you eliminate the interpretation gaps that kill productivity. Junior developers know exactly what to build. Senior developers can spot architectural problems before they happen. Code reviews focus on implementation quality, not requirements clarification.

This is why issue tracking becomes the critical mechanism in the AI development era. It’s not just about organizing work – it’s about creating the structured communication layer that lets human judgment guide AI capability.

But here’s the consequence nobody talks about: when you can generate detailed specs this quickly and AI can turn those specs into working code just as fast, your commit velocity explodes. Teams are seeing 3-5x more commits per day.

Which means your CI/CD pipeline, designed for traditional development speeds, suddenly becomes your biggest bottleneck.

More on that challenge next week – because if you’re not rethinking your deployment discipline right now, you’re about to get buried by your own productivity gains.