What is an AI coding agent?

In short

An AI coding agent is a tool that takes a software task in plain language and carries it out step by step: it reads the code, edits files, runs terminal commands and checks the result itself. Claude Code, Codex and Gemini CLI are well-known examples.

How it works

An agent runs in a loop. It reads the task, decides on the next step, calls a tool (read a file, search, edit, run a command), looks at the result and keeps going until the job is done or it needs to ask you something. Each result goes back to the model as context. However strong the model is, the agent can only do as well as its tools and the context it sees allow.

Agents vs. autocomplete and chat

All three can run on the same models; the difference is who drives the work.

Autocomplete suggests the rest of the line you’re typing. You stay in charge.
A chat assistant explains code or writes a snippet. Copying and applying it is up to you.
A coding agent moves around the repo on its own, changes several files, runs the tests and tries to fix what fails.

Terminal agents

Most popular agents today are CLI tools that run in a terminal: Claude Code, Codex, Gemini CLI, Copilot CLI, Cursor Agent, opencode, Aider, Goose, Qwen Code and others. Each signs in with its own account, uses its own models and handles permissions its own way. Most can connect to external tools through MCP and read instruction files in the project, such as CLAUDE.md or AGENTS.md.

Where agents struggle

The weak spots are fairly predictable, and most of them are handled with a clear task and a check at the end.

Vague tasks: if you don’t say what you want, the agent fills the gaps with guesses.
Long sessions: the context window fills up and older details get summarized or lost.
Early “done”: an agent saying it finished isn’t proof. Tests and review are.
Permissions: an agent that can run commands can also run the wrong one, so approval settings matter.

Coding agents in AgentVera

AgentVera runs 18 coding agents from your machine in real terminals, side by side. Every project gets its own grid, and when you reopen the app the agents return to their conversations from each CLI’s local history.

Grids go from 1×1 to 5×4, and the layout is remembered per project.
Agents waiting for approval share one queue; ⇧⌘A jumps to the one that has waited longest.
Group view brings one group of agents to the front, as terminals or summary cards, and lets you message the whole group.
With flows, an agent’s reply goes to the next agent when its turn ends (Pro and Team).

FAQ

What’s the difference between a coding agent and autocomplete?

Autocomplete suggests how to finish the line you’re writing. A coding agent takes a whole task, reads and changes files itself, runs commands and checks the outcome.

Which AI coding agent is best?

There’s no single answer. Agents differ in models, speed, price, permission handling and tool support. Giving the same task to two agents and comparing the results is often the quickest way to decide.

Does a coding agent send my code anywhere?

The files an agent reads go to its model provider, since the model usually runs in the cloud. Check each tool’s documentation for exactly what is sent and where.

Can I run several coding agents at once?

Yes. To keep them from overwriting each other, give them separate pieces of work or run each one in its own git worktree.