Is multi-agent better than a single agent?

Not by default. Multiple agents on the same problem produce contradictory output and conflicts, and usually do worse than one focused agent. Multi-agent wins only when the work is genuinely parallel and independent, or when you want an independent reviewer separate from the builder.

When should I use multiple AI agents?

When tasks are independent enough to run in isolation (each in its own git worktree), or when you want a separate agent to review another's work. Not when several agents would be editing the same files or reasoning about the same problem — that's the anti-pattern.

Why do multiple agents on one task fail?

They overwrite each other's changes, give contradictory recommendations, and make it impossible to record a coherent account of what happened. Coordination between agents is the hard part, and a swarm on one problem maximises conflict while minimising the benefit of parallelism.

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Multi-agent vs single agent: when more agents help

More agents sounds like more power — and on one problem it's usually the opposite. When each actually wins.

Stuart LeoJune 8, 20263 min read

"Use more agents" is the most over-applied idea in agentic development. It sounds obviously right — if one agent helps, a team of them should help more. So people spin up a swarm on a single feature and are surprised when it goes worse, not better.

The truth is more interesting: multi-agent is sometimes a big win and often a trap, and the line between the two is sharp. Here's when more agents help, when a single agent wins, and how to tell which situation you're in.

The appeal of more agents

A single agent is serial — it does one thing at a time. The dream of multi-agent is parallelism: split the work, run agents at once, finish faster. And there's a real version of that dream. Different models also have different strengths, so the fantasy extends to "the right model on each part."

The appeal isn't wrong in principle. The problem is where people apply it — almost always to a single, shared problem, which is exactly where it backfires.

The anti-pattern: many agents, one problem

Here's the trap, plainly: multiple agents working the same surface at the same time is an anti-pattern. They edit the same files and overwrite each other. They reason about the same problem and reach contradictory conclusions. And because there's no single coherent story of what happened, you can't even write a session brief to record it.

The research on running multi-agent coding workspaces keeps landing here: the hard part isn't spawning agents, it's coordinating them — and a crowd on one problem maximises the coordination cost while delivering little of the parallel benefit.

Stuart Leo

A swarm on one problem isn't parallelism. It's several confident agents disagreeing in the same files.

When multi-agent actually helps

So when does it pay? Two clear cases:

Genuinely independent work. Tasks that don't touch each other — separate features, separate services — can run in parallel, each agent isolated in its own git worktree. The key word is independent. No shared files, no shared problem.
Independent review. One agent builds, a separate bench agent reviews. Here the second agent's whole value is being separate — fresh eyes the builder doesn't have.

Notice both cases avoid the trap: the agents either work on different things, or do different jobs. They're never crowding the same problem at the same time.

Sequential review beats parallel swarm

Even when you use more than one agent, the most reliable rhythm isn't parallel — it's sequential. Lead executes, then a bench agent reviews, then the lead acts. One after another, each step clean and recordable. That sequence preserves the single coherent story that makes the work trustworthy and capturable. A parallel swarm destroys exactly that.

How to decide

A quick decision rule:

Situation	Use
One problem, one surface	Single agent (lead)
Independent tasks, no shared files	Multi-agent, isolated in worktrees
Need an unbiased check	Single builder + bench reviewer (sequential)
Tempted to swarm one hard problem	Single agent — swarming makes it worse

Default to one agent. Add agents only when the work is truly independent or when you specifically want independent review — and even then, run them in a clean sequence, not a free-for-all.

Multiple agents help when work is parallel and independent — not when they're all arguing over the same file.

Start here: see how agent teams work, parallel agents with git worktrees, or read the method.

FAQ

Is multi-agent better than a single agent?: Not by default. Multiple agents on the same problem produce contradictory output and conflicts, and usually do worse than one focused agent. Multi-agent wins only when the work is genuinely parallel and independent, or when you want an independent reviewer separate from the builder.
When should I use multiple AI agents?: When tasks are independent enough to run in isolation (each in its own git worktree), or when you want a separate agent to review another's work. Not when several agents would be editing the same files or reasoning about the same problem — that's the anti-pattern.
Why do multiple agents on one task fail?: They overwrite each other's changes, give contradictory recommendations, and make it impossible to record a coherent account of what happened. Coordination between agents is the hard part, and a swarm on one problem maximises conflict while minimising the benefit of parallelism.

AI agent teams, explained: lead, bench, specialist

When one agent isn't enough you reach for a team — but more agents on one problem makes things worse, not better. The roles that actually work, and why.

Run parallel agents with git worktrees

Several agents on one repo means merge chaos — unless each gets its own worktree. How git worktrees give parallel agents isolation, and how to merge cleanly.

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