What Is an AI Coding Agent? A Developer's Guide
An AI coding agent is a program that writes, modifies, and manages code autonomously. You describe what you want in natural language — “add user authentication to this Express app” — and the agent handles the implementation: reading your codebase, planning changes, writing code, running tests, and creating commits.
This is different from autocomplete (like Copilot) or chatbots (like ChatGPT). Agents take actions. They don’t just suggest — they execute.
How AI coding agents work
The typical agent loop:
- Understand — Read the codebase, parse the file structure, understand dependencies
- Plan — Break the task into steps, decide which files to modify
- Execute — Write or edit code, create files, update imports
- Verify — Run tests, check for compilation errors, validate the changes
- Iterate — If something fails, fix it and try again
- Commit — Save the changes with a descriptive commit message
This loop runs autonomously. You give the agent a task, and it handles steps 1-6 without further input (though you can intervene at any point).
The major AI coding agents
Claude Code
Anthropic’s terminal-based agent. Runs as a CLI tool — type claude in your terminal. Reads your entire codebase, handles multi-file changes, runs commands, and manages git. Currently the most capable terminal agent for complex tasks.
OpenAI Codex CLI
OpenAI’s terminal agent. Similar approach to Claude Code — runs in the terminal, reads your project, generates and modifies code. Strong at code generation tasks.
Aider
Open-source AI pair programmer. Supports multiple LLM backends (Claude, GPT-4, local models). Unique features: explicit file context management, architect mode, git-aware commits. The most mature open-source option.
Open Code
Lightweight open-source terminal coding assistant. Smaller scope but fast. Good for focused tasks that don’t need a full agent loop.
Agents vs copilots vs chatbots
| Chatbot | Copilot | Agent | |
|---|---|---|---|
| Where it runs | Browser/app | Inside your editor | Terminal |
| What it does | Answers questions | Suggests code inline | Writes, tests, commits |
| Scope | Conversation | Current file | Entire codebase |
| Actions | None | Insert text | Run commands, modify files |
| Autonomy | You do everything | You accept/reject suggestions | Agent does the work |
| Example | ChatGPT | GitHub Copilot | Claude Code |
Chatbots talk about code. You paste snippets, they explain or generate. You copy the output back.
Copilots suggest code as you type. They predict the next line or block. You accept or tab past.
Agents do the work. You describe the task, they handle the implementation end-to-end.
Why agents run in the terminal
AI coding agents need to:
- Read files from your filesystem
- Write and modify files
- Run shell commands (tests, builds, git)
- Access your project directory
The terminal is the natural interface for all of these. It has direct filesystem access, can spawn processes, and handles I/O streams. Building an agent as a CLI tool means it works on any machine with a terminal — no editor plugins, no browser tabs, no IDE lock-in.
Running agents effectively
A single agent in a single terminal is useful but limited. You prompt, wait, review, prompt again. The waiting time adds up.
The multi-agent workflow changes the economics: run several agents in parallel using a multi-terminal workspace like GridTerm. Prompt one, switch to the next, prompt that one. By the time you circle back, the first is done.
This requires:
- Multiple visible terminals — Grid layouts so you can see all agents
- Workspaces — Save your agent setup and restore it in one click
- File browsing — Inspect changes without leaving the terminal
- Screenshots — Share visual context with agents
Getting started
- Install an agent:
npm install -g @anthropic-ai/claude-code - Navigate to your project directory
- Run
claude - Describe a task in natural language
- Watch the agent work
Once you’re comfortable with one agent, try running multiple in parallel with a multi-terminal workspace.