Software now writes itself.

With large language models, what once took teams of engineers and weeks of effort can now be done in minutes. A working feature, a full-stack app, even a complex backend system—generated from a single prompt.

Some call it vibe coding.

But if you’ve tried building this way, you’ve probably noticed something: the hard part wasn’t the code.

It was never about the code

The real challenge in software development has always been deciding what to build.

Not how to structure a loop or name a variable. But what product to make, what feature to prioritize, what experience will actually matter to your users.

Code was a constraint. Now it’s not. That just puts the spotlight back on the real bottleneck: knowing what’s worth building in the first place.

The hidden cost of building the wrong thing

We’ve all used software that felt... off.

Apps with features no one uses. Tools that technically work but don’t actually help. Systems that look polished but solve the wrong problem entirely.

These aren’t engineering failures. They’re product failures. Failures of focus. Of listening. Of alignment.

And they’re the most expensive kind—because they waste not just time and money, but opportunity.

AI didn’t fix that. In fact, it may have made it worse.

AI speeds up everything—including mistakes

When code becomes cheap, the real cost is misdirection.

AI lets you build faster than ever—but if you’re moving in the wrong direction, you’ll just get lost faster too. The risk of building something useless is no longer tied to development velocity. It’s tied to decision quality.

This is the paradox of AI-generated software: it removes the friction of building, but leaves the hardest decisions untouched.

The world is louder, not clearer

Today, every product team is swimming in input.

Designs, metrics, bug reports, customer feedback, system logs, brainstorms, requests, analytics dashboards. Every tool is shouting. Every team member is contributing. Every decision has five new data points.

But more input doesn’t mean more clarity. It means more noise.

And the real work—the valuable, irreplaceable work—is turning that noise into signal. Finding the thread. Defining the why. Capturing the what.

A new role for builders

You don’t need to write the code anymore.

You need to decide what’s worth building. You need to listen better, frame better, specify better. You need to observe your systems, your users, your team—and distill all of that into clear, actionable intent.

That’s the job now.

To curate. To define. To guide the machine.

What we believe

The hardest part of building software today is deciding what to build.

Not because we lack ideas—but because we’re drowning in signals: product usage, user behavior, team input, system metrics, support tickets, strategy docs, dashboards. It’s all there—just unstructured, unactionable, and constantly changing.

We believe this decision layer is the next frontier of software development.

And just as we use models to write the code, we can use them to help surface the right opportunities, frame the right tasks, and define what should exist in the first place.

Superagent exists to turn that overwhelming flow of product and team activity into clear, structured, high-quality instructions—so your coding agents build the right thing.

The code ships itself.
Superagent figures out what it should be shipping.