Coding agents have become a regular part of how I build things — and this portfolio is a good example. A lot of the setup, automation, and content here was built with the help of AI coding agents.
What Is a Coding Agent?#
A coding agent is more than a code autocomplete tool. Instead of just suggesting the next line, an agent can take a task, read through your codebase, make changes across multiple files, run commands, and iterate until the task is done.
The key difference from a regular AI assistant is autonomy. You describe what you want, and the agent figures out the steps — reading files, writing code, and fixing its own mistakes along the way.
How I Use Them#
My main tool is Claude Code, which runs directly in the terminal and has access to my project files. I use it for:
- Setting up new features from scratch (like the Dify RAG sync workflow)
- Writing GitHub Actions workflows I’ve never written before
- Debugging configuration issues where I’m not sure what’s wrong
- Generating first drafts of blog posts and refining them together
The workflow feels like pair programming. I describe the goal, the agent proposes an approach, I approve or redirect, and it executes.
What Actually Works Well#
The biggest win is crossing unfamiliar territory faster. As a student, I often need to work with tools I’ve never used before. An agent lets me get something working first and understand it second — which turns out to be a great way to learn.
It’s also good at tasks that are tedious but not creative — boilerplate, config files, repetitive edits across files. Things I could do myself but would rather not.
What Doesn’t Work#
Agents struggle when the goal is vague. If I say “make the site look better”, that goes nowhere useful. The more specific and concrete the task, the better the result.
They also can’t tell if something feels right — a layout change might be technically correct but visually off. For anything UI-related, I still have to check it myself.
What I Learned#
Using coding agents has made me better at breaking work into clear, concrete tasks. That skill turns out to be useful whether you’re talking to an AI or a teammate. The agent forces you to be precise about what you actually want — which is a surprisingly good habit to build.