AI agents are changing the way we build, debug, and ship code. Inspired by powerful tools like Cursor, I set out to build a lightweight, terminal-based AI Coding Agent. It integrates with the OpenAI API, takes natural language commands, executes them using predefined tools, and returns intelligent responses. Along the way, I learned a lot—about coding, prompt design, modularity, and cross-platform robustness.

Here's what I built, and the core lessons I walked away with.

🤔 What is the AI Coding Agent?

The idea was simple but powerful:
Create an agent that thinks step by step like a developer, performs tasks like setting up a React app, editing files, running commands, and even launching Chrome—all using natural language prompts.

It follows a structured execution loop:

Start → Plan → Action → Observe → Next Plan → … → Final Output

It uses tools like:

• create_react_project

• write_to_file, append_to_file, read_file

• run_command, run_command_bg

• open_in_chrome

This loop is orchestrated using OpenAI’s API and a well-thought-out system prompt.

Key Learnings from the Project

1. Key Takeaways from the Project

Building this agent taught me how crucial it is to slow down and plan before writing code.

You can’t expect the model (or yourself) to perform tasks without knowing:

• What is being asked?

• What are the intermediate steps?

• Which tools are needed for each step?

Clear thinking → Clear planning → Smooth execution.

2. Consider Edge and Border Conditions (OS Compatibility)

   One tricky part was writing code that worked across Mac, Windows, and Linux.

   Take this example:

   def open_in_chrome(url):

   system = platform.system()

   if system == "Macp": # Bug: Should be "Darwin"

   ...

Small mistakes like OS name mismatches could break the agent. The solution? Think ahead. Test across platforms. Add fallbacks.

This reinforces a universal truth in development:

“If it’s not tested across all scenarios, it’s not really working.”

3. Invest Time in the Prompt

   The SYSTEM_PROMPT is the brain of the agent. It defines how the assistant should:

   • Interpret user queries

   • Select the next tool

   • Format its responses in a structured JSON format

   • Think in steps

I learned that prompt engineering is product design in disguise.

🛠️ A good prompt isn’t just about instructions.

It’s about enabling the model to make decisions like a real developer.

4. 🧱 Build with Modularity in Mind

   Instead of one giant function to do everything, I created:

   def write_to_file(...)

   def append_to_file(...)

   def create_react_project(...)

   def run_command(...)

Why?

• It’s reusable.

• It’s testable.

• It’s readable.

• It’s scalable.

The agent became a composition of small Lego blocks, each doing one thing well. This made debugging and updating the agent much easier.

Final Thoughts

This wasn’t just a fun experiment. It was a glimpse into the future of AI-assisted development.

By combining:

• Careful step planning

• Robust cross-platform tooling

• Smart prompt design

• Modular programming principles

…I created a lightweight but effective AI agent that feels like a coding partner.

And the best part?

You can keep improving it—make it handle full-stack apps, deploy to cloud, analyze bugs, generate tests, or even commit code to GIT.

Thanks for reading!

If you enjoyed this post or built something similar, I’d love to hear about it.
Let’s connect and share ideas on the future of intelligent coding agents.