AI Coding Agents for QA: Part 1 — What They Are and Why It Matters

Matviy Cherniavski

19d (edited) • AI&QA

AI is everywhere, and it's easy to feel overwhelmed.

Codex. Claude Code. Cursor. Windsurf. Copilot. New names every week, new hype every day.

But they all describe the same concept: AI coding agents.

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𝐖𝐡𝐚𝐭 𝐈𝐬 𝐚𝐧 𝐀𝐈 𝐂𝐨𝐝𝐢𝐧𝐠 𝐀𝐠𝐞𝐧𝐭?

Simple: it's a tool that interacts with AI and generates code. That's it. But like any tool in a QA engineer's kit, not all of them are equal.

Some are great for specific tasks, some are poor at most things, and some are solid generalists you can use anywhere and get good results.

I spent over $3,000 testing them so you don't have to. In this series of posts I'll share exactly what I found. Today, we start with the fundamentals.

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🧠 𝐖𝐡𝐚𝐭 𝐈𝐬 𝐚𝐧 𝐋𝐋𝐌?

LLM stands for Large Language Model, the brain powering every AI coding agent.

But here's the key thing to understand: you never talk to the LLM directly. There's always a tool sitting in between:

► YOU ► Tool (Cursor / Copilot / Claude Code) ► LLM (GPT-5 / Claude / Gemini)

The same pattern applies when you use AI chat apps, except the interface is built for conversation, not code.

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⚡ 𝐖𝐡𝐲 𝐓𝐡𝐢𝐬 𝐌𝐚𝐭𝐭𝐞𝐫𝐬 𝐟𝐨𝐫 𝐘𝐨𝐮

The tool (cursor, etc) you pick is responsible for roughly 50% of your results.

Here's why: the tool reads your code, decides what information to send to the LLM, and determines how much the AI actually understands about your project and how it can write the actual code.

Different tools. Different developers. Different quality. Same LLM. Wildly different output.

This is exactly why the same engineer, using the same LLM but a different tool, can get completely different results. For example, using the exact same ChatGPT LLM in Cursor versus Copilot for the same task will produce very different quality output.

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📌 𝐊𝐞𝐲 𝐓𝐚𝐤𝐞𝐚𝐰𝐚𝐲𝐬

LLM = the brain. You can't access it directly.
Tools (Cursor, Copilot, Claude Code) sit between you and the LLM.
The tool accounts for ~50% of the quality you get.
Different tools, different quality, different output even with the same LLM underneath.

In Part 2, I'll break down each tool, compare them head-to-head, and tell you which ones actually work.

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To use AI coding agents effectively, you must know programming and understand proper test framework architecture. Start learning today and get ready for the next stage of QA: AI Automation Engineers.

👉 Go to the Classroom tab and click QA AutoTest Accelerator to get started. https://www.skool.com/qa-automation-career-hub/classroom

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