Forget Autocomplete, Let's Talk Auto-Action: Getting Started with Agentic Coding & the Gemini CLI
Z
Zack Saadioui
8/11/2025
Forget Autocomplete, Let's Talk Auto-Action: Getting Started with Agentic Coding & the Gemini CLI
Alright, let's have a real talk. For years, we've been hearing about how AI is going to "change coding." And for years, that's mostly meant slightly better autocomplete & some fancy code suggestions. Helpful? Sure. A revolution? Not quite.
But what if your AI didn't just suggest the code? What if it understood your goal, created a plan, wrote the code, tested it, & even fixed its own mistakes, all from a single prompt in your terminal? That's not a suggestion engine; that's an agent. An autonomous partner.
Welcome to the world of agentic coding. It’s here, & honestly, it's a bigger deal than most people realize. Google quietly dropped an open-source tool called the Gemini CLI, & it's not another autocomplete on steroids. It's a terminal-based AI agent that reasons through problems, uses tools, & handles the grunt work while you focus on the big picture. This isn't about replacing developers; it's about turning them into conductors of an AI orchestra.
This is the stuff we were promised. So, let's pull back the curtain & get you started with what feels like the next chapter of software development.
So, What Is Agentic Coding, Really?
Before we dive into the nuts & bolts of the Gemini CLI, let's clear something up. What does "agentic" even mean?
Think of it this way:
Traditional AI Assistant: You ask it, "How do I write a function in Python to read a CSV file?" It gives you a block of code. You then copy it, paste it, adapt it, & run it. The AI's job is done after the suggestion.
Agentic AI: You tell it, "Find all the CSV files in the
1
/data
directory, read them, merge them based on the 'user_id' column, & then write a new summary file that shows the total purchase amount for each user."
The agentic AI doesn't just spit out code. It thinks. It creates a plan, like:
"Okay, first I need to list the files in the
1
/data
directory."
"Then, I'll need to write a loop to open & read each CSV."
"I'll use a library like Pandas to handle the data."
"I'll perform the merge & the calculation."
"Finally, I'll write the result to a new file called
1
summary.csv
."
It then executes that plan, using the tools it has—like your shell, a file reader, or even a web browser—to get the job done. This is the "Reason and Act" (ReAct) loop you might hear about, & it's the core of what makes these tools so powerful. The AI is no longer just a passive knowledge base; it's an active participant in your workflow.
Meet Your New Coding Partner: The Gemini CLI
Google's Gemini CLI is an open-source AI agent that brings this agentic power right into your terminal. It's not embedded in a bloated IDE or hidden behind a complex interface. It lives where many developers live: the command line.
Here’s why it’s turning heads:
It's Genuinely Agentic: It uses that ReAct loop to tackle complex, multi-step tasks autonomously. You can give it a high-level goal, & it will figure out the steps to get there.
HUGE Context Window & Free Tier: With a personal Google account, you get access to Gemini 2.5 Pro with its massive 1 million token context window. You can get up to 60 requests per minute & 1,000 per day for free. That means it can understand a VERY large chunk of your project's codebase without breaking a sweat.
Built-in Tools: It comes with the essentials right out of the box. It can read folders (
1
ls
), find files (
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glob
), read file contents (
1
read-file
), search within files (
1
grep
), write new files (
1
write-file
), & even run shell commands (
1
shell
). It also has built-in Google Search to ground its answers in real-time information.
Multimodal Powers: This is where it gets pretty cool. You can give it a PDF of a design spec or even a sketch of an app's UI, & it can use that to generate the application code.
Open Source & Extensible: The whole thing is open source under an Apache 2.0 license, which is a big deal for transparency & trust. Plus, it supports something called Model Context Protocol (MCP) servers, which is a fancy way of saying you can plug in new tools & capabilities, like connecting to GitHub or generating images with models like Imagen.
Getting Your Hands Dirty: Installation & Setup
Alright, enough talk. Let's get this thing installed. It's surprisingly straightforward.
Prerequisite: Node.js
The main thing you need is Node.js, specifically version 18 or higher (though v20+ is recommended). If you don't have it or aren't sure, the easiest way is to use Node Version Manager (nvm).
If you have a Mac with Homebrew, it's even easier.
Installation
You've got two main options:
The Quick & Easy Way (npx): If you just want to try it out without a permanent install, run this in your terminal. It fetches & runs the latest version on the fly.