From Prompt to Passing Test: A Complete Agentic QA Session

Sound familiar? In the first article, we set up a project scaffold designed for AI. But a good structure only gets you so far if the AI is just a code suggester. Useful, but not transformative. You still have to know what to ask, verify what it wrote, adapt it to your project, and repeat for every file. In the second article, we saw what makes an AI agent different from a chatbot. It reads your code, takes actions, and works inside your project. But here's the catch: an agent is only as good as the instructions it follows. In the third article, we saw how CLAUDE.md gives the agent its rules and workflow. But rules without depth only get you so far. "Use the Page Object Model" is a rule, but how exactly do you structure a page object? What's the difference between a locator getter and an action method? How do you compose components into page objects? In the fourth article, we gave the agent deep expertise through skill files. Now it knows how to build page objects, selectors, and fixtur