How to Run Multiple AI Agents in Parallel
To run multiple AI agents in parallel without chaos, you need four things: a backlog split into independent tasks, an isolated workspace per agent so they can't overwrite each other, one screen where you can see and steer everything, and a review step that checks the actual work. Get those four right and one person can direct a pod of agents; skip any of them and parallelism turns into cleanup.
This is the workflow as Orca implements it — but the steps hold for any serious parallel-agent setup. If you're still on "one agent, one task, babysit, copy-paste, wait, repeat," this is the way out of that loop.
Step 1: Split the backlog into independent tasks
Parallelism starts on paper, not in the terminal. Go through the list and mark which tasks can run without touching each other: different features, different files, different repos, different problems entirely. Those are your parallel lanes. Tasks that genuinely depend on each other stay sequenced — an agent picks up the second one after the first one's commit lands.
A useful test: could two contractors do these two tasks in the same week without a meeting? If yes, they can run in parallel.
Step 2: Give every agent an isolated workspace
This is the step people skip, and it's the one that decides whether parallel agents ship or collide. Orca dispatches each task to an agent in its own isolated git worktree — a separate working copy of the codebase on its own branch (orca work). Eight agents can edit "the same file" simultaneously because each is editing its own copy; the work merges through commits you review, not through agents overwriting each other live. The full failure catalog — and why worktrees solve it — is in how to keep parallel agents from stepping on each other.
Step 3: Launch the pod
With lanes defined and isolation handled, spin up the agents. In Orca that's one of two moves:
orca orchestrate— open the command center and spawn agents interactively: 8+ Claude Code sessions visible on one screen, each on its own job.orca fleet— point Orca at a JSON config and launch N agents at once, each on an independent problem. Same pod, zero clicking.
And if part of your workflow isn't an AI agent at all — a build, a server, a deploy script — orca attach wraps any terminal program as a controllable worker so you drive it alongside everything else.
Step 4: Brief each agent like a hire, not a search box
Each agent gets an outcome, the context a smart new hire would need, and a definition of done. Vague briefs are the number-one way parallel setups die: eight agents amplify a sloppy brief eight times. Orca lets you do this at the speed you think — brief the pod out loud with /dictate or /voice and it talks back, hands-free. The briefing method itself is a skill worth ten minutes: Plain English Prompts covers it end to end.
Step 5: Turn on autopilot and get out of the loop
Manual parallelism still leaves you as the dispatcher — every finished task waits for you to notice and assign the next one. Orca's autopilot (/auto) removes that job: it picks up the next task the moment one lands, surfaces each commit as it ships, and spot-checks the actual work — not the agent's summary of it. You get pulled in for real decisions only. Direct, don't babysit.
Step 6: Review diffs, not vibes
When an agent reports done, the evidence is the diff. Orca surfaces commits as they land so you review actual changes, then re-prompt or advance. This is also where the self-healing matters: an agent that's stuck or off track gets harpooned, and Orca reuses the slot instantly with a fresh agent — the pod keeps a full crew working while you review at your own pace.
What makes this workable at 8+ agents?
Eight agents, one screen — spawn, steer, harpoon, recycle; talk to the whole pod and it talks back.
Two quiet enablers under the workflow. First, model consistency: every Orca agent runs on Claude Opus with a 1M-token context window, so the pod stays consistent and nothing loses the thread mid-task. Second, economics: every agent runs on the Claude Code subscription you already pay for — no metered API tokens — so scaling from one agent to a pod doesn't scale a bill alongside it. What that throughput change is actually worth is worked through in what serial AI work costs.
FAQ
How many AI agents should I start with?
Start with two or three genuinely independent tasks and grow from there. The ceiling is set by how independent your tasks are and how good your tooling is at isolation and review — Orca runs 8+ Claude Code sessions from one terminal once you're comfortable.
Do parallel agents multiply my AI costs?
Not with Orca. Every agent runs on the Claude Code subscription you already pay for — not metered API tokens — so a pod in parallel doesn't mean a meter climbing per agent.
Can I run parallel agents on non-coding work?
Yes, if the work lives in files an agent can act on — research writeups, copy, data wrangling, docs. Orca's agents are Claude Code sessions, which handle any file-based work, and orca attach can wrap any terminal program as a controllable worker in the pod.
What if two tasks genuinely depend on each other?
Don't parallelize them — sequence them. Give the dependent task to an agent after the first one's commit lands. Parallelism pays on independent work; forcing dependent tasks to run simultaneously just manufactures conflicts.