In my last post I talked about how I spent a week heads down using AI to work on a greenfield engineering metrics tool. As I built it, I’d often navigate the web app and spot things that needed to be fleshed out. Sometimes it was a small typo; other times it was a bigger feature that was still TODO.
At one point I had Claude Code redesign the homepage to make it more lively. In doing so, it added some new functionality that didn’t fully exist yet: A “View All Insights” link that would show you all the AI-generated analyses about a given pull request or piece of work. Since I hadn’t actually built the page for it yet, it led to a 404.
Traditionally, fixing this would kick off a whole sequence of events. I’d have to scope out the feature, think about the UI, define the API needs, and write a detailed ticket. Then, I’d need to build the backend endpoint, create the UI components, and wire everything together. It’s a linear, manual process.
Instead, I took a different approach. I ran a single custom command to generate a ticket for the new page. This command invoked several specialist sub-agents (you can find their .md definitions in the appendix)—a product-manager , a ux-designer , and a senior-software-engineer —who worked in parallel to flesh out the requirements. The result was a fully-formed ticket, created in minutes.
Here’s a quick preview of the actual ticket these agents generated in Linear:
With the plan defined, I could then feed that ticket into another command that kicks off the implementation agents ( senior-software-engineer , code-reviewer , etc.).
This workflow changes the dynamic. What would normally take hours of planning, spec’ing, and building was done asynchronously while I focused elsewhere. If the agents get it wrong, I don’t really care—I’ll just fire off another run. The cost of failure is so low that optimizing for speed and taking more “shots on goal” is the right call.
This entire process—from planning to implementation—ran in the background across multiple terminals while I moved on to the next task. This is what true parallelization looks like; the agents were so active they even started hitting API rate limits.
The Core Principles of an Agentic Workflow
My workflow is built on three core principles. Understanding them will help you apply this approach to your own tasks.
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