How to use parallel agents (multi-agent workflows)
Corbin Brown
2 min read
For the first couple of years, AI coding meant one conversation at a time: you and an agent, taking turns. The current shift is bigger — editors like Cursor now let you run several agents at once, each on its own task, while you rotate between them like a manager doing rounds. Done right, an afternoon produces what used to take a week. Done wrong, three agents produce three conflicting versions of your project. The difference is knowing what parallelizes.
What parallelizes (and what collides)
The rule fits in one sentence: parallel agents work when tasks don't touch the same files. A landing page, a fix in the payment flow, and new tests live in different corners of the codebase — perfect fan-out. Two agents editing the same component will overwrite each other like two people typing in one document. This is why serious multi-agent setups give each agent an isolated copy of the project (Cursor calls these worktrees; cloud agents get whole separate machines) and deliver results as reviewable changes you merge one at a time.
Your first multi-agent session
- 1Commit first. A clean GitHub save point before fan-out means any agent's work can be dropped without ceremony.
- 2Pick three tasks from different corners — independent by construction, each described like a ticket you'd hand a contractor: what, where, and what done looks like.
- 3Launch, then rotate. Check on each agent as it finishes; review and merge one result at a time rather than accepting all three blind. Merging one at a time turns three risky changes into three routine ones.
- 4Keep a cap. Two or three parallel agents is the sweet spot to start — the bottleneck isn't the agents, it's your review attention, and quality of review is what keeps speed from becoming slop.
FAQ
Does running parallel agents cost more?
You pay for the tokens each agent uses, so three agents burn roughly three tasks' worth — the same work, compressed in time, not free. The real cost to watch is review debt: work finishing faster than you can review it means merging blind, which is how parallel speed turns into parallel mess.
Can the agents talk to each other?
In most current tools, no — they work independently and you're the coordinator. That's a feature at this stage: independent tasks with human merges beats agents negotiating with each other. Orchestration where a lead agent delegates to workers exists (Boost OS's /spawn is that pattern), but the merge decision staying human is what keeps it safe.