How to Run Multiple AI Coding Agents in Parallel

To run multiple AI coding agents in parallel, give each agent its own terminal pane, its own working directory, and its own git branch, then keep them all on one screen so you can watch and review them. Starting one agent is easy; the real problem is running several without losing track of what each one is doing.

This is a practical guide to doing that well. We’ll walk through the common approaches honestly — terminal tabs, tmux, git worktrees, and a purpose-built workspace — with concrete patterns for one-agent-per-task, side-by-side watching, and racing two agents on the same problem. The goal is throughput you can actually trust, not just more agents.

Why running one agent is easy and running four is hard

A single agentic coding session is simple: you open a terminal, launch the agent, hand it a task, and read the output as it works. The trouble starts the moment you want a second one going while the first is busy. Now you’re juggling:

• Attention. Two agents finish at different times, ask questions at different times, and stall at different times. You have to know which is which at a glance.
• Collisions. If they share one checkout, they edit the same files and step on each other’s changes, and the combined diff is impossible to trust.
• Review. Output arrives faster than you can read it, so it gets tempting to merge on faith — which is exactly how subtle bugs ship.

Every approach below is really an answer to those three problems: keeping agents visible, keeping their work isolated, and keeping the result reviewable.

Approach 1: Multiple terminal tabs or windows

The obvious first move is to open another tab and launch a second agent. It works for about two sessions. Past that it breaks down fast:

• You can only see one tab at a time, so you cycle through them hunting for the one that needs you.
• Every tab points at the same project directory by default, so the agents overwrite each other unless you set up isolation by hand.
• There’s no shared view of who’s done, who’s stuck, and who’s waiting on a question.

Tabs are fine for launching a quick one-off agent. They are not a system for running a fleet.

Approach 2: tmux split panes

tmux is the natural next step. It splits one terminal into a grid of panes so you can see several agents at once instead of cycling tabs. A minimal setup looks like this:

# start a session, split it, and launch an agent per pane
tmux new -s agents
# split vertically (side by side), then horizontally
Ctrl-b %        # vertical split
Ctrl-b "        # horizontal split
Ctrl-b o        # cycle between panes

That solves visibility — you can watch four agents on one screen. What tmux does not give you is isolation or review. Every pane still defaults to the same checkout, so you have to manage separate working directories yourself, and there’s nothing to help you read each agent’s diff before it lands. It tiles terminals; it doesn’t know anything about agents.

Approach 3: git worktrees so agents don’t collide

The fix for collisions is to stop sharing one checkout. Git worktrees let one repository have several working directories at once, each on its own branch, so each agent edits its own files and produces its own clean diff:

# one checkout per agent, each on its own branch
git worktree add ../feature-auth -b feature-auth
git worktree add ../feature-search -b feature-search
git worktree list

Now you point agent A at ../feature-auth and agent B at ../feature-search. They never touch the same files, both diffs stay reviewable on their own, and you can throw away anything that didn’t work out with a single git worktree remove. This is the backbone of safe parallel agent work — we go deep on it in git worktrees for AI coding agents.

Concrete patterns that work

Once you have panes plus isolation, a few patterns cover almost everything you’ll want to do:

• One agent per task. Give each pane a single well-scoped job — one feature, one bug, one refactor — in its own working directory and branch. Clear boundaries keep the output easy to follow and easy to merge.
• Watch them side by side. Keep every session on screen so a stalled agent or a question is obvious. Parallelism only pays off if you can see all of it at once.
• Race two agents on one task. Hand the same prompt to two different agents in two panes with two working directories, let both finish, then compare the diffs and keep the stronger result. Claude Code and Codex often solve the same problem differently, and choosing the better answer is cheaper than getting one right on the first try.
• Pipeline a task. Run one agent to build and a second to review or write tests against the first one’s branch, so the two stages overlap instead of waiting on each other.

Approach 4: a workspace built for this

Tabs, tmux, and worktrees each solve one piece — visibility, panes, isolation — but you end up wiring them together by hand and maintaining the seams. A dedicated agentic development environment brings the pieces into one tool so running multiple AI coding agents in parallel is the default, not a setup project.

Kadro ADE is that workspace. It runs Claude Code, Codex, opencode, and 20+ coding agents on the desktop, each in a real terminal pane, so you can launch a different agent per task and keep them all on one screen. You split a workspace into the layout the work needs, color-code the panes, and save the layouts you reuse. When the grid gets tight, Canvas mode lets you pan and zoom across panes on an infinite surface, with a live browser parked next to the agents building for it. Per-provider usage meters show your session and weekly spend at a glance, so a fleet of agents doesn’t quietly burn through your limits.

The point isn’t more agents for their own sake. It’s keeping the work visible and reviewable as you scale up, so the throughput is real and the call on what ships stays yours.

Frequently asked questions

Running agents in parallel is the fastest way to get more out of agentic coding — as long as you can see them and trust their diffs. Download Kadro ADE to run your agents in split panes on one screen, or read what agentic coding is for the bigger picture.