How Do AI Coding Agents Like Cursor Work?

The 'Brilliant Intern' Mental Model: What an AI Agent Actually Does

Here's the best analogy I know: imagine hiring a brilliant intern who can read your entire project in two seconds, never forgets anything they've read, and will attempt any task you give them — but they need clear instructions and will occasionally make confident mistakes.

That's an AI coding agent in a nutshell.

Under the hood, tools like Cursor and Claude Code follow a loop called **Observe → Plan → Act → Check**:

1. **Observe** — The agent reads your files, your folder structure, and any error messages currently on screen. 2. **Plan** — It breaks your request into smaller subtasks (e.g., 'create a file, then write a function, then import it'). 3. **Act** — It writes or edits code, runs terminal commands, or installs packages — real actions, not suggestions. 4. **Check** — It looks at the result. If something failed, it tries to fix it automatically before telling you.

This loop is called an **agentic loop** — 'agentic' just means the AI takes initiative rather than waiting for you after every single step. This is fundamentally different from ChatGPT, which only *talks* about code. These agents *touch* your project files directly.

Your first step right now: open Cursor, type 'Add a button to my homepage that says Hello,' and watch the agent read your files before it writes a single line. Notice how it explores first. That's the Observe step in action.

Step-by-Step: What Happens the Moment You Send a Prompt

Let's make this concrete. Here's exactly what happens inside Cursor or Claude Code the moment you type 'Build me a contact form':

1. **Context gathering** — The agent scans your project. It reads your folder names, existing files, and any framework you're using (like React or plain HTML). This is called building a 'context window' — essentially its short-term memory, which holds around 100,000–200,000 words depending on the model. 2. **Intent parsing** — It interprets what you actually want. 'Contact form' triggers it to think: fields, validation, a submit button, maybe an email handler. 3. **Tool selection** — Agents have built-in tools: file reader, file writer, terminal runner, web search (in some setups). It picks which tools it needs. 4. **Execution** — It writes the form code into the right file. If it needs a new file, it creates one. If a package is missing, it runs `npm install` in the terminal automatically. 5. **Self-review** — It re-reads what it wrote, checks for obvious errors, and often fixes them before showing you the result.

Here's the key insight most beginners miss: **the agent's quality is directly tied to how much context it has.** A vague prompt gives it a small context. A prompt like 'Add a contact form to my React app — I'm using Tailwind CSS and I want the fields to be Name, Email, and Message' gives it a rich context and produces dramatically better results.

Try this now: give Claude Code your most specific prompt yet and compare the output to a vague one. The difference will surprise you.

The Biggest Misconception About AI Agents (And the Mistake It Causes)

Most guides position AI coding agents as 'smart autocomplete.' That framing is wrong, and it leads to the single most common beginner mistake: **treating the agent like a search engine instead of a collaborator.**

When you ask Google something, you get a list of links. When you ask an AI agent something, it takes action. Those are completely different relationships.

Here's how that misconception causes real problems:

- **Mistake 1: Vague one-liners.** Typing 'fix my app' gives the agent almost no context. It guesses — and guesses wrong. Recovery: always tell it *what's broken, what you expected, and what you see instead.* - **Mistake 2: Blind trust.** Agents are confidently wrong sometimes. Claude Code once removed a working database connection while 'simplifying' a file. Recovery: use git (a tool that saves snapshots of your code) before letting the agent make big changes. In Cursor, press `Ctrl+Z` immediately if something breaks. - **Mistake 3: Stopping after one prompt.** Agents work best in conversation. If the first result is 80% right, say 'That's close — now make the button blue and add a thank-you message after submit.' Each follow-up refines the output.

The counterintuitive truth: **the agent doesn't need you to know how to code. It needs you to know what you want.** Your job is product manager, not programmer. The clearer your vision, the better the output.

Cursor vs. Claude Code: Which Agent Should You Actually Use?

Both tools use large language models (the same AI brains powering ChatGPT), but they're built for slightly different workflows. Here's a plain-English comparison:

| Feature | Cursor | Claude Code | |---|---|---| | Where you work | Inside a code editor (like VS Code) | Terminal / command line | | Best for | Visual editing, seeing changes live | Larger tasks, full-app builds | | Beginner friendliness | ⭐⭐⭐⭐⭐ | ⭐⭐⭐ | | Autonomy level | Medium — asks before big changes | High — acts more independently | | Price to start | Free tier available | Usage-based via Anthropic API |

**My honest recommendation:** Start with Cursor if you've never touched a terminal. Its interface looks like a normal app, you can see edits highlighted in real time, and it asks permission before doing anything drastic. Move to Claude Code once you're comfortable with the idea of an agent running commands on your machine — it's more powerful for building entire features in one shot.

Key Takeaways

• AI coding agents follow a 4-step loop — Observe, Plan, Act, Check — and repeat it automatically until the task is done or they get stuck.
• The context window (your prompt + your project files) is the agent's entire working memory. Claude Code's window holds up to ~200,000 words — so a detailed prompt genuinely outperforms a vague one by a measurable margin.
• Surprising insight: you don't need to understand the code the agent writes to use it effectively. Knowing *what you want* matters more than knowing *how it's built* — most experienced users treat agents as product decisions, not technical ones.
• Before letting any agent make large edits, type 'git init' and 'git commit -am save' in your terminal to create a snapshot. This takes 10 seconds and lets you undo anything in one command.
• Within 2 years, agentic tools will handle 80%+ of routine coding tasks end-to-end — the developers who thrive will be the ones who learned to give great prompts and review AI output critically, not those who memorized syntax.

FAQ

Q: Do AI coding agents actually run code on my computer, or just suggest it?
A: Both Cursor and Claude Code can execute real commands on your machine — they write files, install packages, and run your app. For example, Claude Code can run 'npm start' to launch your project and read the error output automatically.

Q: Does this actually work for real projects, or just toy demos?
A: It works on real projects — with an honest caveat: agents perform best on clearly scoped tasks under ~500 lines of new code. For a 50,000-line legacy codebase with no documentation, they still struggle with deep architectural decisions, though they handle isolated features well.

Q: How do I start using an AI coding agent today if I have zero experience?
A: Download Cursor from cursor.com — it's free to start and installs like any normal app. Open it, create a new folder, and type 'Build me a webpage that says Hello World with a blue background' in the chat panel on the right.

Conclusion

AI coding agents work because they combine a powerful language model with real tools — file access, terminal control, and a self-correcting loop — turning your plain-English goals into working software. The magic isn't in the AI alone; it's in the feedback loop between your clear instructions and the agent's ability to act on them instantly. Your single doable next step: install Cursor today, open a blank folder, and give it your most specific prompt yet — something like 'Build me a one-page portfolio site with my name, three fake projects, and a contact button.' Watch the whole Observe-Plan-Act-Check loop run live. You'll understand more in five minutes of doing than in five hours of reading.

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