How to Run Multiple AI Coding Agents at Once

AI coding agents are fast, but they still pause. They ask for confirmation, hit an error they need you to triage, or finish a task and wait for the next one. If you're only running one agent at a time, those pauses mean you're sitting idle. Running multiple agents in parallel is the obvious fix, and it works. But it introduces a coordination problem that most developers underestimate.

Why Run More Than One Agent at a Time?

A single AI coding agent can only work on one task at a time. Meanwhile, you probably have a backlog: a bug fix in the API, a new component in the frontend, test coverage for that module you shipped last week. Each of those is a clean, isolated unit of work that an agent can handle independently.

Running multiple agents lets you parallelize across those tasks. Start Claude Code on your backend refactor in one terminal, spin up Aider on the test suite in another, and let Cursor's terminal mode handle the UI component. Instead of waiting for one to finish before starting the next, all three make progress at once.

The productivity gain is real. In practice, developers running two or three agents in parallel report getting through their task list two to three times faster, not because each agent is faster, but because idle time between prompts gets eliminated.

The Setup: Terminals, Tmux, and Agent Sessions

The simplest approach is multiple terminal windows. Open three tabs, start a different agent in each, and arrange them on your screen. This works fine for two sessions. Beyond that, it gets messy.

Tmux is the better option for anyone comfortable with the terminal. Create a session per agent, name them clearly (tmux new -s api-refactor, tmux new -s frontend-tests), and switch between them with Ctrl-b s. You get persistence, easy switching, and your sessions survive if your terminal crashes.

If you prefer a GUI approach, iTerm2 on macOS supports split panes and named tabs. The key is labeling each session with the task, not the tool. "auth-fix" tells you more than "claude-3" when you're juggling context.

For agents that run inside editors, like Cursor or Copilot, you can open multiple VS Code windows pointed at different project directories. Each runs its own agent instance with its own context.

The Real Problem: Knowing Which Agent Needs You

Here's what actually breaks down with multi-agent setups. It's not the tooling or the configuration. It's attention. Three agents running at once means three processes that might need your input at any moment. One is waiting for you to confirm a file deletion. Another hit a type error and is asking how to proceed. The third finished its task two minutes ago and you didn't notice.

Every minute an agent sits idle waiting for you is a minute wasted. And the worst part is that you don't know it's waiting unless you actively check. So you end up tab-cycling compulsively, scanning each terminal for a prompt, which destroys whatever focus you had on the task you were actually reviewing.

This is the core tension of parallel agent workflows: you want to stay focused on one thing, but you also need to know when the others need you. Most developers handle this by either over-checking (constant tab switching, no deep focus) or under-checking (long idle gaps where agents sit waiting). Neither works well.

Monitoring Agent State Without Tab-Checking

The fix is getting notified when an agent needs input, instead of polling for it yourself. Pulser does exactly this. It's a free macOS menubar app that watches your agent sessions and sends you a notification the moment any agent pauses for input.

With Pulser running, you can focus on reviewing the diff from Agent A without worrying about whether Agent B or C is blocked. When one of them needs you, you'll know. No tab-checking, no mental overhead. You respond, give it the next instruction, and go back to what you were doing.

This changes the workflow from "constantly monitor everything" to "respond to what matters." It's the difference between polling and event-driven design, and the same principle applies to your attention. For a deeper look at why idle time matters so much, see our breakdown of the agent idle time problem.

A Practical Multi-Agent Workflow

Here's a concrete example. Say you're building a feature that touches the API, the frontend, and the database schema. You break it into three tasks:

Agent 1 (Claude Code, tmux session "api"): "Add the new /projects endpoint with validation and error handling. Write tests for it."

Agent 2 (Aider, tmux session "frontend"): "Create a ProjectList component that fetches from /projects and renders a card grid with loading and error states."

Agent 3 (Claude Code, tmux session "migrations"): "Write the migration to add the projects table with the columns defined in schema.md. Include a rollback."

You kick all three off, then focus on reviewing Agent 3's migration since it's the riskiest. Agent 1 finishes its tests and asks you to review. You get a notification, switch to that session, approve the approach, and give the next instruction. Back to reviewing the migration. Two minutes later, Agent 2 has a question about the card layout. Notification, quick answer, back to focus.

Over 30 minutes, you've moved three tasks forward instead of one, and you never lost focus to compulsive tab-checking. Each tool has different strengths, so matching the right agent to the right task matters.

Tips for Staying Productive Across Sessions

Keep tasks isolated. If two agents are editing the same files, you'll spend more time resolving conflicts than you save. Break work into independent units that touch different parts of the codebase.

Give each agent clear, complete instructions. The more context you front-load into the prompt, the longer the agent can work before needing you. Include file paths, expected behavior, and edge cases.

Start with two agents, not five. Running too many sessions creates more coordination overhead than it saves. Two or three is the sweet spot for most developers. Scale up once you have a rhythm.

Name your sessions by task, not by tool. You'll switch context faster when your terminal says "auth-bug" instead of "session-3."

Review diffs carefully. Running agents in parallel makes it tempting to rubber-stamp approvals so you can move to the next thing. Don't. The time saved by parallelism buys you space to review each output properly. Use it.

Multi-agent workflows aren't complicated to set up. The hard part is managing your attention once they're running. Solve that, and you unlock a genuine multiplier on your output.