29 October 2025
Coding with Agents: My Experiences with Vibe Coding
Coding assistants are evolving at remarkable speed. Here's what it's actually like to work with the latest AI coding agents across multiple projects.
Coding assistants are evolving at remarkable speed. Where we once had simple text completion or suggested snippets in an IDE, we now see autonomous coding agents that can be assigned a task, generate pull requests, and work independently for hours. Over the past couple of months I have experimented with several of these agents across a range of projects. So, what is it actually like to work in this new way, and what might it mean for the future of coding?
Testing the Top Coding Agents
After trying a number of different assistants, here are my reflections on my three favourites.
replit.com: A Cloud IDE with Built-in Agents
This is a pure online IDE with agents built in. It spins up its own environments on cloud VMs and works entirely within them. The interface is intuitive, and navigating the file system feels natural. It is particularly strong for architecture deployment, since it controls the entire environment, and it integrates well with GitHub. Hosting is included, so deployment is one click.
GitHub Copilot: Seamless Integration with Your IDE
This integrates seamlessly with IDEs such as Visual Studio and Visual Studio Code, and its GitHub integration is obviously excellent. You can assign tasks directly to Copilot and leave it to work. The first time I set a bug fix task before finishing for the evening and returned the next morning to find a pull request waiting felt like a genuine milestone.
Claude Code: A Command Line Powerhouse
This is a command line agent that is surprisingly straightforward to use. Avoid the add-ins for VSCode, as running it in the terminal is far better. It has strong GitHub integration, and the terminal-first approach seems to give it extraordinary versatility.
On balance, I found myself using Claude Code most often. Sonnet 4.5 consistently felt like the best model, and it performed particularly well when used with Claude Code.
How Coding Agents Changed My Workflow
The biggest impact was that they made me more ambitious. Suddenly it felt possible to turn almost any idea into working software within minutes. For simple ideas, that really is the case. My first attempt was an SEO analysis tool. Built in replit, it was live on the web in about 20 minutes.
More complex projects, however, highlight the current limitations. I tried building an AI dashboard designer with minimal scaffolding. I made rapid progress at first, but soon ran into a problem that caused the agent to loop, misdiagnose issues, and make sweeping changes that were not required.
Lessons Learned When Working with Coding Agents
Do not be afraid to start again. Check in code regularly. As soon as the agent begins to struggle or veer off track, revert to the last working checkpoint and restart with a clean context.
You are the system architect. It is your responsibility to design the overall architecture. Agents are your junior developers. Start small, discard what does not work, and refine.
The Future of Coding with AI Agents
What we are seeing is the next stage in the evolution of programming languages. Similar claims were made when compiled languages replaced assembly, and again as successive generations brought us closer to natural language.
Each leap has made coding more accessible, broadened what is possible, and increased the number of people able to participate. Coding agents are simply the next step, but on a much larger scale.