What are AI coding agents?

What is an AI coding agent?

An AI coding agent is a program that uses a language model to complete a software task by taking actions in your project, not just by suggesting text. You give it a goal such as “add pagination to the orders API and cover it with tests.” The agent decides which files to open, what to change, which commands to run, and when it is done.

Most coding agents today are agent CLIs: command-line programs you start inside a repository. Claude Code (Anthropic), Codex (OpenAI), Gemini CLI (Google), GitHub Copilot CLI (GitHub), OpenCode, Grok CLI (xAI), Qwen Code and MiMo Code (Xiaomi) all work this way. They run in a terminal, read the files in the current directory and use your account with the vendor.

How is an agent different from autocomplete or AI chat?

An agent acts on your project; autocomplete and chat only suggest. Autocomplete predicts the next lines where your cursor is. A chat answers questions and gives you code to paste. An agent opens files, edits them, runs your test suite and reacts to the result without you copying anything.

How do AI coding agents work?

AI coding agents work in a loop: the model reads context, chooses a tool, the tool runs, and the result goes back to the model. Tools are simple and concrete — read a file, search, write a file, run a shell command. The loop repeats until the task is done, a limit is hit or the agent asks you something.

1. 1

   Read the goal

   You describe the outcome. Good prompts name the files, the constraints and how to check the result.

2. 2

   Explore the code

   The agent searches the repository and reads the files that matter. Project notes such as CLAUDE.md or AGENTS.md give it house rules.

3. 3

   Plan and edit

   It decides on an approach and writes changes to files on disk.

4. 4

   Run commands

   It runs the build, the tests or a script and reads the output.

5. 5

   Fix and repeat

   If something fails, it reads the error, changes the code and runs again.

6. 6

   Report

   It stops and summarizes what it changed. What you get is a set of file changes — a diff — ready for review.

Which AI coding agents can you use today?

The most used AI coding agents are CLIs from model vendors and open-source projects. The table lists the ones Tallos can install and sign in to from its Connections page, with the command you run and how sign-in works.

GitHub Copilot CLI and dozens of other agent CLIs also run in Tallos from its agent picker. The agents differ in approach, defaults and the models behind them, not in the basic loop. For a side-by-side of the three most asked-about, read Claude Code vs Codex vs Gemini CLI.

How much autonomy should an AI coding agent have?

An agent should have as much autonomy as the place it runs can safely contain. By default most agent CLIs ask before running shell commands or editing files. Each one also has a flag that skips those prompts so the agent can work without stopping.

• Manual approval — the agent asks before risky actions. Safer, but you have to watch it.
• Autonomous mode — the agent acts without asking. Faster, and useful when the task runs in an isolated checkout you will review.
• Scoped setups — per-project settings, allowed commands and hooks let you tune the middle ground in agents that support them.

Tallos starts agents in manual mode by default, so they ask before running commands. Autonomous mode is opt-in: turn on Let agents run commands without asking under Advanced on the welcome screen, or change it later in Settings → Agents → Agent Permissions — it pairs well with the separate git worktree each task already runs in. You can also edit one agent's launch arguments. Keep in mind that a worktree isolates files from other tasks; it is not a security sandbox, and the agent can still reach whatever its process can.

How do I start using AI coding agents?

Start with one agent you already have access to, one small task and a test command it can run. Install the CLI, sign in, open your repository, describe the outcome, and review the diff it produces before you commit. Tallos does the install and sign-in from one screen — see connect your agents.

Install and sign in to your agents from one Connections page. Tallos uses the subscriptions you already have.

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What are the limits of AI coding agents?

AI coding agents are limited by context, by the checks they can run and by the fact that one session works on one thing at a time. Knowing the limits is how you get good results from them.

• Context window — an agent can only hold so much text at once. In large codebases it may miss a file or forget an early instruction in a long session.
• Confident mistakes — agents can produce code that compiles, reads well and is wrong. Review is not optional.
• Weak signals — without tests, a linter or a way to run the app, the agent cannot tell if it succeeded.
• Drift — long, vague tasks wander. Small tasks with a clear finish line work better.
• Rate limits and usage — every vendor meters usage. Long runs can hit limits mid-task.
• One thing at a time — a single agent session is sequential. While it works, you wait.

The last limit is the one an ADE removes. You cannot make one agent think faster, but you can give several agents separate tasks at once.

How do you run several AI coding agents at once?

You run several agents at once by giving each one its own copy of the repository and its own terminal. Two agents in the same folder will overwrite each other's changes. A git worktree per task solves that: each agent gets its own branch and files, and you compare or merge the results at the end.

You can set this up by hand with git worktree and a terminal multiplexer, or use an ADE that does it for you. In Tallos, each task opens in a new worktree with the agent you pick, supported agents open in a readable native chat view, and a status indicator tells you which agent is working, done or waiting on you. The full walkthrough is in how to run AI agents in parallel.

When the work is bigger than one task, a squad puts a leader agent in charge: it splits the objective, starts member agents in their own worktrees, checks their results and reports back for your approval.