Are AI Coding Agents Like GitHub Copilot and Cursor Worth the Hype? My Firsthand Experience

Why I Explored AI Coding Agents

Over the past few weeks, I’ve spent focused time using GitHub Copilot and Cursor AI to understand how they fit into my everyday app development work. There’s been a lot of buzz around AI agents in developer communities, and I wanted to evaluate them hands-on. Do they actually help, are they secure? My goal was to find out whether these tools could enhance my coding flow, reduce repetitive effort, and improve code quality, all while being safe and practical to use.

What Are AI Coding Agents?

AI coding agents, at their core, are tools powered by large language models like GPT-4 or Claude. Unlike basic autocomplete, these tools understand a broader context: they can read across files, follow your instructions, debug issues, and even refactor multiple parts of your project intelligently. You can describe what you want in plain English, and these agents translate it into code. They operate in a smart feedback loop understanding your context, taking action, and continuously refining suggestions based on your responses.

Working with GitHub Copilot

I started experimenting with GitHub Copilot in VS Code while working on React and Python projects. At first glance, it felt like a smarter autocomplete. But the more I used it, the more I realized how well it adapted to the structure and purpose of the project. For example, when I added notes to an instructions.md file, Copilot began aligning its suggestions with those guidelines. It did a good job catching common syntax or logic errors and offered inline fixes. I could ask it to optimize a function, and it would suggest cleaner or more efficient code. Copilot Chat was surprisingly helpful when I needed to understand what a shell command did or when I got stuck with a terminal error. It also created simple unit tests based on my function names and comments, which sped up my testing workflow.

Exploring Cursor AI

Meanwhile, Cursor AI felt like stepping into a future version of the IDE. Built on top of VS Code, Cursor integrates AI deeply into the development experience. I was able to highlight code, ask Cursor what it does, and get immediate, accurate answers. I often switched between GPT-4 and Claude depending on the complexity of the task. One of the standout features was Composer mode, which presents multiple action types like Ask, Edit, or Agent. Each mode is suited for different situations, whether I was making a small change, rewriting a block, or automating a repetitive refactor. Cursor also handled multi-file edits gracefully. When I wanted to insert logging statements across multiple modules, it understood the intent and made the necessary updates across the project. The built-in web search saved me from having to leave the IDE to hunt down documentation it pulled relevant snippets directly into the chat.

How Students Can Benefit from AI Coding Agents

From my perspective, students can benefit a lot from these tools. They often struggle with understanding syntax, structure, or debugging areas where AI agents can assist without judgment. Instead of spending hours piecing together answers from forums, students can get real-time help directly in their IDE. They can ask questions like "What does this loop do?" or "Why is this error happening?" and get immediate responses. However, it's essential that they take time to learn and not just rely on copy-pasting code. Understanding the suggestions is just as important as using them.

How App Developers Can Use Them Effectively

For industry professionals like myself working on mobile and web app development, these tools have real productivity gains. Whether it’s scaffolding a new feature, fixing bugs, writing documentation (I love auto complete for writing comments), or cleaning up legacy code, the combination of Copilot and Cursor made the process faster and smoother. When exploring a new library or onboarding into a new codebase, being able to ask questions inline or get instant summaries of functions made a tangible difference. It reduced the mental load and allowed me to stay focused on delivering business value.

Security Risks and Considerations

That said, there are still risks to be aware of. These tools rely on cloud models, and that means there’s a chance your code or parts of it are being sent to external servers. There’s also the risk of licensing violations, especially if the AI suggests code that was learned from copyrighted or restrictive repositories. And of course, AI isn’t always right. Sometimes it suggests code that looks good but doesn’t work, introduces subtle bugs, or isn’t performant.

How to Use AI Tools Safely

To use these tools responsibly, I’ve adopted a few guidelines. I avoid using them in sensitive or private repositories unless I’ve sandboxed the environment. I disable telemetry or data sharing whenever possible. I also make sure to scrub any secrets or API keys before sharing code context. Most importantly, I treat AI suggestions as drafts, not final answers. I always review and test the code before merging anything.

Choosing Between GitHub Copilot and Cursor

When deciding which tool to use, it really depends on the task. If I just need to fill in repetitive code or write boilerplate logic quickly, GitHub Copilot does the job well. For larger-scale refactoring, deeper debugging, or when I need to ask questions about the structure of a project, Cursor is far more capable. Both tools support test generation, but Cursor gives more control and clarity in how it explains changes. Copilot is great when switching between terminal usage and application logic, while Cursor excels when you want to explore and reshape your codebase.

Final Thoughts and What’s Next

Looking back, I believe tools like GitHub Copilot and Cursor AI are transforming how we build software. They’re not just speeding things up they’re changing how we interact with our code. That said, they should be seen as assistants, not replacements. You still need to lead the process, understand the code, and decide what goes into production.

In my next post, I’ll be diving deeper into the world of AI agents, task automation, and tools built using Model Context Protocol (MCP). If you're interested in advanced agents or AI-enhanced developer workflows, stay tuned. I’ll be sharing more hands-on explorations soon. Feel free to reach out if you have questions, or if you’d like help setting up these tools in your own development environment.