The first time most developers run a second AI coding agent alongside the first, it’s almost by accident — one terminal tab for a bug fix, another opened out of curiosity for a small feature, and suddenly two things are happening at once. It works, sort of, until the two agents’ changes need to come back together and nobody remembers which one touched what.
Orchestrating multiple AI agents well is less about tooling and more about a handful of habits that keep parallel work legible. Orchestrating multiple AI agents means giving each one a clearly scoped task, its own isolated workspace, and a defined point where its work rejoins the rest of the codebase. This guide walks through that from scratch — no prior multi-agent experience assumed.
Start With One Task, One Agent
Before orchestrating anything, get comfortable with a single agent finishing a single, well-scoped task end to end: understanding the request, editing files, running tests, and producing a diff you’d actually merge. If a solo agent struggles to stay on task or wanders into unrelated files, adding a second agent won’t fix that — it will just give you two unpredictable diffs instead of one.
Once a single agent’s output is reliably reviewable, you have the real prerequisite for going parallel: tasks specific enough that an agent can’t accidentally drift into another agent’s territory. Vague instructions like “improve the app” produce vague, overlapping diffs. Specific instructions like “add input validation to the signup form” produce a diff you can review in two minutes.
This is also a good time to build a mental model of how long a given task actually takes an agent, because orchestration is partly a scheduling problem. A task that takes an agent ninety seconds and a task that takes fifteen minutes both look identical before you start them, but running five of the slow kind in parallel means five diffs landing at once, all needing review in the same window. Running a mix of quick and slow tasks staggers that load naturally.
The Three Things Every Orchestrated Agent Needs
Once you’re ready to run more than one agent, each session needs three things before it starts:
- A scoped task. One clear objective, not a bundle of unrelated changes. If you’re tempted to use “and” in the task description, consider splitting it into two agents.
- An isolated workspace. A separate git worktree and branch per agent, so file edits from one session can never collide with another’s, even accidentally.
- A review checkpoint. A moment — after the agent finishes, before you merge — where you actually look at the diff rather than trusting it blindly.
Skip any one of these and orchestration degrades back into the “accidental parallelism” that got messy in the first place.
A Simple Orchestration Workflow
# Spin up an isolated workspace per task
git worktree add ../repo-signup-validation -b agent/signup-validation
git worktree add ../repo-rate-limit -b agent/rate-limit-api
# Point one agent session at each directory, give each a scoped task,
# and let them run independently — neither can see or touch the other's files
# When an agent finishes, review its branch like any PR
cd ../repo-signup-validation && git diff main
# Merge one at a time, resolving real conflicts explicitly
git checkout main && git merge agent/signup-validation
Notice there’s no special orchestration software in that sequence — just worktrees, branches, and disciplined review. That’s intentional. Most of what people call “agent orchestration” for a small team or solo developer is really just applying normal git hygiene consistently across several sessions instead of one.
That said, the manual bookkeeping does start to add friction once you’re past two or three concurrent sessions — remembering which directory maps to which task, which branches are still waiting on review, and which ones are ready to merge. This is the point where a lot of people either build themselves a small tracking spreadsheet or reach for a tool that keeps that state visible without extra typing. Neither is required to get the benefits of orchestration, but both save real time once the session count climbs.
Common Orchestration Mistakes to Avoid
A handful of mistakes account for most of the frustration people report when they first try running several agents at once:
- Assigning overlapping tasks. Two agents both told to “improve error handling” in the same module will produce two diffs that fight each other at merge time. Split tasks along file or feature boundaries instead, so the overlap is structurally impossible rather than something you’re hoping doesn’t happen.
- Skipping the review checkpoint because things “looked fine.” The whole point of orchestration is that you’re running more work in parallel, not that you’re reviewing less of it. Each agent’s diff still needs the same scrutiny a single agent’s would get.
- Letting task descriptions grow mid-session. It’s tempting to add “and also fix this while you’re in there” to a running agent’s task. That’s how a scoped, reviewable task turns into a sprawling diff that’s hard to evaluate.
- Forgetting which worktree belongs to which task. Once you’re past two or three parallel sessions, descriptive branch and directory names stop being a nice-to-have and become the only way to keep track of what’s running where.
Most of these come down to the same root cause: treating orchestration as a way to do more at once rather than a way to do the same disciplined work across more tasks simultaneously. The habits don’t change: they just need to hold up across N sessions instead of one.
How Many Agents Can You Actually Run at Once?
There’s no fixed limit, but a practical ceiling shows up fast: the number of diffs you can carefully review in a sitting. Running five agents in parallel produces five diffs that all land around the same time, and reviewing five diffs carelessly defeats the point of reviewing at all.
| Agent count | What it demands of you | Good starting point for |
|---|---|---|
| 1 | Full attention on one task | Learning what good agent output looks like |
| 2–3 | Task scoping, sequential review | Most solo developers, most days |
| 4+ | Strong task boundaries, a sidebar or dashboard to track sessions | Larger refactors split into independent chunks |
Two or three well-scoped agents running in parallel is a comfortable starting point for most people — enough to feel the speedup without losing track of what each one is doing.
Getting Started
- Pick two small, unrelated tasks from your backlog — ones that clearly won’t touch the same files.
- Create a separate worktree and branch for each, and run one agent session per worktree.
- When both finish, review each diff on its own branch before merging, treating it like a PR from a very fast, very literal teammate.
- Once that feels comfortable, download Agents — it manages the worktree-per-session bookkeeping for you from a single sidebar, with a diff panel built in, so you can scale from two agents to several without the manual git commands.
Orchestrating multiple AI agents isn’t a special skill so much as it’s ordinary git discipline applied a little more deliberately — scoped tasks, isolated workspaces, and a review step you don’t skip. The underlying idea has a name in computer science, multi-agent systems, and the same principle holds here as it does there: independent agents coordinate best when their boundaries are explicit, not assumed. Get that rhythm down with two agents and scaling to more is mostly a matter of confidence. For a closer look at what these agents actually are before you start stacking several of them, see what is an AI coding agent, and visit Agents when you’re ready to run them from one place.