Agile LLM Rethink

In partnership with

From Agile to AI: How the Idea-to-Code Pipeline is Being Rewritten

For years, we’ve had a standardized way of converting ideas into software: Agile. The process was designed to take a user’s need—something often fuzzy and high-level—and distill it into clear, buildable pieces of work. Tickets were the currency of translation. "As a dispatcher at a trucking company, I need to update my delivery logs so I can track delays." Clean, structured, and tied to value.

Then the product owner would step in to break that idea down further. Maybe it was too big. Perhaps it touched too many systems. Engineers would weigh in, debate edge cases, and settle on implementation details. Eventually, code was written. It would then flow back up the chain: the developers demoed the work, the product owner reviewed it, and the loop closed with a yes or a no. Language moved down and back up again.

This process worked, but it wasn’t fast. Translating an idea took time, meetings, documentation, and assumptions. Even in a clean system, a lot of nuance lived in the heads of product managers and developers.

Then came LLMs.

Now, the founder doesn’t need to write a perfect Agile ticket. They can converse with an AI and begin breaking down the idea in real-time. They can explore variations, ask about edge cases, and iterate on the architecture to refine it. The act of breaking down a story isn’t just an engineering task anymore—it’s a prompt engineering task.