Running Multiple Claude Instances in Parallel

The first time I waited twenty minutes for Claude to finish implementing a feature — only to realize it went in the wrong direction — I thought:

There has to be a better way.

There is.

Instead of running one AI assistant sequentially, I run multiple Claude Code instances in parallel. Each works on the same problem in isolated git worktrees. When they finish, I compare results and keep the best parts.

The Core Insight

AI assistants are not deterministic.

Same prompt.
Same model.
Different results.

Sometimes the first attempt is excellent.
Sometimes it drifts.
Sometimes it confidently builds the wrong abstraction.

If you work sequentially, each attempt is a gamble.
You wait, evaluate, retry.

Parallelizing removes the waiting loop.

Why Git Worktrees

Branches alone aren’t enough. They are just pointers.

Git worktrees create:

• Separate working directories
• Each with its own checked-out branch
• Sharing the same repository history

That means you can have multiple implementations evolving simultaneously without stashing or context switching.

Same repo.
Same base commit.
Multiple active solutions.

My Workflow

I use a small helper command:

git parallel "add-auth" --agents 3

This:

1. Creates three worktrees:
   • add-auth-1
   • add-auth-2
   • add-auth-3
2. Launches Zellij
3. Starts Claude Code in each pane

Then I:

• Give each instance the same prompt
• Let them run in parallel
• Come back later to compare outputs

What Happens in Practice

Typically:

• One implementation is overengineered
• One misses edge cases
• One is clean and well-structured

Sometimes:

• Each has a strong idea worth keeping
• The final solution becomes a synthesis

Instead of restarting the same assistant three times, I evaluate three candidates.

Total elapsed time is roughly the same as one attempt.

The quality of outcome is consistently better.

Where This Works Best

This approach shines when:

• Designing new features
• Refactoring complex modules
• Exploring architectural directions
• Working in ambiguous problem spaces

It is unnecessary for:

• Small bug fixes
• Mechanical changes
• Deterministic tasks

Parallelization only makes sense when multiple valid solutions exist.

The Mindset Shift

The mistake is treating AI as an oracle.

It isn’t.

It is a probabilistic generator.

The better framing:

You are running experiments.

Each agent instance is a sample from a distribution of possible implementations.

Your job is evaluation and selection.

This turns AI assistance from a linear conversation into a parallel search process.

Tradeoffs

Is it more resource-intensive?

Yes.

Does it consume more tokens?

Yes.

But developer time is expensive. Waiting is frustrating. Context switching is costly.

I would rather review three implementations once than restart one implementation three times.

Parallel AI feels closer to how we approach distributed systems:

Spawn workers.
Collect results.
Select the best outcome.

It turns uncertainty into leverage.