AI coding agents are starting to feel like teammates.
You ask one to refactor a module.
Another to write tests.
A third to prototype a feature.
Individually, they’re powerful.
But the moment you try to run them in parallel, things get messy.
This article is about why that happens — and what I learned trying to fix it.
The Multitasking Problem
Running one AI coding session is simple.
Running three at the same time usually looks like this:
- Three terminal windows
- Multiple feature branches
- Manual context switching
- No clear overview of what each agent is doing
Technically, it works.
Cognitively, it doesn’t scale.
The friction appears in small ways:
- You forget which terminal is working on which branch.
- You accidentally reuse context.
- You lose track of long-running operations.
- You hesitate to spin up “just one more” agent because overhead increases.
The CLI gives you power — but no structure.
Why tmux (Alone) Isn’t Enough
You can improve layout with tools like tmux.
That helps visually.
But it doesn’t solve workflow structure:
- You still manually create branches.
- You still manage isolation.
- You still need to remember which session owns which task.
- You still lack higher-level organization.
Layout is not workflow.
And when working with AI agents, workflow matters more than layout.
The Key Insight
The breakthrough for me was this:
Each AI session should behave like an isolated feature branch — automatically.
Not just another terminal pane.
A structured environment.
If AI agents are “teammates,” then each one deserves:
- Its own workspace
- Its own git worktree
- Clear visual boundaries
- Easy creation and disposal
That reduces cognitive load dramatically.
Designing for Parallel AI Work
I started experimenting with a simple idea:
What if running multiple AI coding agents felt more like managing multiple feature branches in a visual workspace?
Instead of:
Terminal A
Terminal B
Terminal C
You get:
- Agent Session 1 → Feature branch + isolated worktree
- Agent Session 2 → Separate branch + separate worktree
- Agent Session 3 → Experimental sandbox
All visible at once.
All running natively.
No API wrappers.
No abstraction layers limiting features.
What I Built
To explore this idea, I built Parallel Code, a desktop app that:
- Runs Claude Code, Codex, and Gemini CLI directly
- Creates git worktrees automatically per session
- Tiles multiple agent sessions in a structured UI
- Keeps full CLI behavior intact
It doesn’t change how the agents work.
It just makes multitasking practical.
Repository:
https://github.com/johannesjo/parallel-code
(Insert GIF demo here)
What Changed in My Workflow
After switching to structured parallel sessions:
- I hesitate less before spawning a new agent.
- Experimental work feels safer.
- Long-running refactors no longer block other tasks.
- Context switching feels deliberate instead of chaotic.
The biggest difference isn’t speed.
It’s clarity.
The Bigger Question
Right now, most AI-assisted development assumes:
One agent. One terminal. One task.
But that might not reflect how we’ll work long-term.
If AI agents become real collaborators, we’ll need better ways to:
- Run them in parallel
- Isolate work safely
- Maintain visibility across sessions
- Reduce mental overhead
We may be in the “single-agent IDE” phase of a multi-agent future.
I’m Curious
If you’re using AI coding agents heavily:
- Do you run multiple sessions in parallel?
- How do you manage isolation?
- Is terminal + tmux good enough?
- Where does your workflow break down?
I’m still iterating on this idea, and I’d appreciate thoughtful feedback.
Top comments (1)
What do you think about this? What is your setup to deal with this?