AI Coding Agents for QA: Part 2 โ Types of the AI Coding Agent
In Part 1 I promised to tell you which tools actually work. Let's start by ruling one category out. โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ ๐ซ ๐๐ญ๐จ๐ฉ ๐๐ฌ๐ข๐ง๐ ๐๐ก๐๐ญ ๐๐ฉ๐ฉ๐ฌ ๐๐จ๐ซ ๐๐จ๐๐ข๐ง๐ ChatGPT, Claude.ai, Gemini โ these are not coding tools. I know. You can paste code into them. You can ask questions. It feels like it should work. But here's the problem: these tools were trained to answer everything. Recipes. Health advice. Legal questions. Your Playwright test suite. Coding task. Those tools treat them all the same way. They also have zero access to your repo. They don't know your folder structure, your test helpers, your naming conventions โ nothing. So every answer is generic. It could fit any codebase, anywhere. Generic = useless for real coding work โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ โฆ๏ธ ๐๐๐ ๐ฏ๐ฌ ๐๐๐: ๐๐ก๐๐ญ'๐ฌ ๐ญ๐ก๐ ๐๐ข๐๐๐๐ซ๐๐ง๐๐? Coding-specific tools split into two types: โบ CLI โ you run them from the terminal, inside your repo โบ IDE โ they live inside your editor (Cursor, VS Code, etc.) CLI means Command Line Interface. You open your terminal, go to your project, and run something like: `>_ claude -p "add a login test to the checkout suite"` The agent reads your actual code, understands your project, and does the work. โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ โฆ๏ธ ๐๐ก๐ ๐ ๐๐๐ ๐๐จ๐จ๐ฅ๐ฌ ๐๐จ๐ฎ ๐๐๐๐ ๐ญ๐จ ๐๐ง๐จ๐ฐ ๐น ๐๐ฅ๐๐ฎ๐๐ ๐๐จ๐๐ built by Anthropic It has three models for three use cases: - Opus โ the most powerful. Complex refactors, hard bugs, architecture decisions. Expensive. - Sonnet โ the daily driver. Fast, accurate, handles most coding tasks and documentation well. - Haiku โ fast and cheap. Good for the small jobs only: renaming files, adding a helper, generating a fixture. Pricing works on a "window" system. You buy a plan ($20 / $100 / $200 per month) and each plan comes with a usage limit. That limit resets every 5 hours and every week. In practice: burn through your limit at 2pm, wait until 7pm for the reset. It sounds annoying. Once you learn to match the model to the task you rarely hit the cap.