How to Run Multiple AI Agents at the Same Time

Running one AI agent at a time is the default. But there's nothing stopping you from running two, three, or five agents in parallel — each working on a different part of your project. Here's how to do it, what to watch out for, and when it makes sense.

Why run multiple agents?

The simplest reason: time. If you have three independent tasks and each takes 5 minutes of agent work, running them sequentially takes 15 minutes. Running them in parallel takes 5.

But time savings aren't the only benefit:

• Specialization — Different agents can tackle different types of work. Use Claude Code for complex refactors, Codex for rapid prototyping, and a shell script for mechanical tasks.
• Review loops — One agent writes code, another reviews it. Immediate feedback without waiting.
• Context separation — Each agent gets a clean context focused on its specific task, instead of one overloaded context trying to juggle everything.

Method 1: Multiple terminal windows

The simplest approach. Open multiple terminals and start an agent in each one.

Terminal 1:

cd ~/project
claude  # Start Claude Code for auth refactoring

Terminal 2:

cd ~/project
claude  # Start Claude Code for API tests

Terminal 3:

cd ~/project
codex   # Start Codex for documentation

This works but has a major drawback: you can only see one terminal at a time (or split your screen into tiny panes). When Agent 1 finishes, you won't know unless you switch to that terminal and check.

Method 2: tmux split panes

tmux lets you tile multiple terminal sessions in one window:

tmux new-session -s agents
# Ctrl+b % to split vertically
# Ctrl+b " to split horizontally

Better than separate windows — you can see multiple agents at once. But tmux panes are fixed grids. With 3+ agents, each pane becomes too small to read comfortably. And there's no visual indication of agent state (working, waiting, done).

Method 3: Canvas-based workspace

The most effective approach for 2+ agents is a visual canvas workspace. Place each agent in a separate tile on an infinite canvas. Arrange them spatially based on their role in your workflow.

Benefits over terminal windows and tmux:

• Resize freely — Give more space to the agent you're currently focused on.
• Agent state indicators — See at a glance which agents are working, waiting, or done.
• Routing connections — Automatically pass one agent's output to another.
• Persistence — Your layout is saved. Close and reopen, everything is where you left it.

MadoHub is built for this — it gives you an infinite canvas where you run terminal-based agents in draggable, resizable tiles with real-time state detection.

What to watch out for

File conflicts

Two agents editing the same file simultaneously will create conflicts. Before running agents in parallel, make sure they're working on different files or modules.

Good parallel split:

• Agent A works on src/auth/ while Agent B works on src/api/

Bad parallel split:

• Agent A and Agent B both modifying src/config/database.ts

Resource usage

Each AI agent session consumes RAM (for the terminal + agent process) and API credits. Running 5 Claude Code instances simultaneously means 5x the API usage. Monitor your credit consumption if you're on a metered plan.

Context pollution

If multiple agents are working in the same git repository, one agent's uncommitted changes can confuse another agent that reads the same files. Solutions:

• Git branches — Each agent works on a separate branch.
• Independent directories — If tasks are truly independent, use separate project copies.
• Sequential dependencies — Agent B only starts after Agent A commits its changes.

When to use multiple agents

The rule of thumb: if you can cleanly divide the work into independent pieces with clear boundaries, multiple agents will be faster. If the work is deeply interconnected, a single agent with full context is better.

Automating the handoff

The biggest friction point with multiple agents is manually moving information between them. When Agent A finishes and you need to tell Agent B what happened, you're copying text, reformatting it as a prompt, and pasting.

Automated routing eliminates this. You define connections between agents ("when A finishes, send the summary to B"), and the system handles extraction, transformation, and delivery.

This turns a manual multi-agent workflow into a pipeline that runs with minimal intervention. You set it up, monitor progress, and intervene only when an agent needs direction.

Getting started

1. Pick two tasks in your current project that are independent.
2. Open two terminals side by side.
3. Start an agent in each.
4. Notice how it changes your attention pattern — you shift from deep-focus on one task to scanning across two.
5. When one finishes, try manually sending its key output to the other.

Once you've done this manually and felt the benefit, you'll understand why automated routing and visual workspaces exist. They make the manual version effortless.