KiroFlow

Inspiration Most AI coding tools are optimized for the first five minutes: you prompt, you get code, it looks plausible. The problem shows up in the next five hours—the code drifts from what you meant, conventions get invented on the fly, and you end up maintaining the agent's output instead of your project. I wanted to build the opposite: a workflow where one prompt becomes a structured spec, and every piece of code that follows is traceable back to that spec. Kiro's spec-driven development, steering files, and agent hooks gave me exactly the primitives to do that, so KiroFlow is my attempt to wire them into a single, coherent loop.

What I learned The biggest lesson was that structure beats raw model capability. A vague prompt with a strong model still yields vague code; a precise spec with a modest model yields something I can actually verify. Writing requirements first forced me to surface edge cases I'd otherwise only discover after the code broke.

I also learned how much leverage comes from consistency mechanisms. Steering files act like a constraint set C C, and the agent searches for a solution x ∈ C x∈C; the tighter and clearer C C is, the smaller the search space and the more predictable the output. Hooks close the loop by making routine work—test generation, doc updates—happen automatically on file save, so consistency isn't something I have to remember to enforce.

How I built it KiroFlow runs a single prompt through a four-stage pipeline:

Prompt → Spec. The raw prompt is expanded into requirements (user stories with acceptance criteria), then a design doc, then a sequenced task list.

Spec → Execution. Kiro's agent implements task by task, with each task scoped small enough to verify independently.

Steering → Consistency. Project conventions—naming, error handling, test style—live in steering files, so the agent follows my patterns instead of inventing its own.

Hooks → Automation. File-save hooks trigger test and doc generation in the background, keeping the repo clean without interrupting my flow.

The core loop is: define intent → execute against spec → verify against acceptance criteria → feed corrections back into steering. Everything in the repo can be traced back to a requirement, and every requirement has a check.

Challenges The hardest part was resisting the urge to skip the spec. With a capable agent, it's tempting to prompt-and-iterate, but that's exactly how you end up with code nobody can reason about. Staying disciplined felt slow at first and paid off later.

The second challenge was spec granularity. Too coarse and the agent guessed; too fine and I was essentially writing the code myself. Finding the right task size—small enough to verify, large enough to be worth delegating—took several iterations, and I tuned it based on where the agent's output drifted most.

Finally, verification was non-trivial. "It runs" is not the same as "it meets the requirement," so I built acceptance checks into each task rather than trusting a successful build. That's also where hooks earned their keep: automated checks on save meant drift got caught early instead of accumulating.