# How to work with several coding agents in parallel > Guide to parallel agent work: slicing independent tasks, orchestrating with Conductor or Codex's cloud and not dying in review. - Canonical: https://serchai.com/en/guides/ai-parallel-agents/ - Site: Serchai (https://serchai.com) — AI tools comparator - Language: en - Updated: 2026-07-25 --- ## Tools you will use - [Conductor](https://serchai.com/en/reviews/conductor/) — Orchestrates several Claude Code agents in parallel from a Mac app. - [OpenAI Codex](https://serchai.com/en/reviews/openai-codex/) — OpenAI's coding agent, included with ChatGPT: local in your terminal or in the cloud. - [Devin](https://serchai.com/en/reviews/devin/) — Cognition's autonomous engineer, now across four surfaces: Desktop (the former Windsurf), Cloud, CLI and Review. ## The steps, in short 1. **Slice the work into tasks that do not collide** — Parallelism works with independent tasks: distribute like teammates who cannot talk to each other. 2. **Pick your architecture: local with Conductor or cloud** — Conductor orchestrates Claude Code on your Mac, and Codex's cloud or Devin executes remotely. 3. **Manage the queue like a coordinator** — Launch, watch from the corner of your eye and review on completion: your role shifts from coding to coordinating. 4. **Respect the real limit: your review capacity** — Launch only what you can genuinely review, because the bottleneck is you. > **TLDR:** The ceiling of working with one agent is the wait: while it works, you watch. Parallelism breaks it with two architectures: Conductor orchestrates several Claude Codes on your Mac with each task isolated in its own repository copy, and Codex's cloud agents (or Devin for batches) execute remotely without occupying your machine. The technique is solved: the real limit is your review capacity, and the whole system gets designed around that neck. This guide is for anyone already fluent with one agent (the base system is in the [first project](https://serchai.com/en/guides/ai-first-agent-project/) guide) who wants to multiply: the task queue grows faster than a serial agent empties it. The presiding warning: parallelism multiplies production and also multiplies errors if the review system does not scale with it. This guide builds both together. ## 1. Slice the work into tasks that do not collide Parallelism starts at the slicing, not the tool. Parallelizable tasks are the independent ones: the invoice view bug, the missing test in the mail module and a dependency upgrade share no files, and can advance simultaneously without conflict. The practical test before launching: will they touch the same files? If the answer is yes or probably, they go serial, because the later merge conflict eats the time parallelism saved. The right intuition is distributing work among teammates who cannot talk: what would you hand each one so they do not get in each other's way. Size matters too: medium, bounded tasks perform better in parallel than epics, because they return sooner, review better and fail cheaper. ## 2. Pick your architecture: local with Conductor or cloud There are two ways to have several agents working, and they do not compete: they complement. The local one: [Conductor](https://serchai.com/en/reviews/conductor/) orchestrates several Claude Codes on your Mac, each task in its own isolated repository copy (git worktrees), with a panel to watch each one's progress, review and merge. It is free and uses your existing Claude subscription: the cost is the quota draining proportionally faster. The cloud one: [Codex](https://serchai.com/en/reviews/openai-codex/)'s agents run each task in a remote environment with a repository copy, launchable from the web or your phone, occupying neither your machine nor your terminal. And for the extreme case of the industrial batch (twenty identical migrations across twenty repositories), [Devin](https://serchai.com/en/reviews/devin/) is built exactly for that, as the [batch migrations](https://serchai.com/en/guides/ai-batch-migrations/) guide develops. The hybrid pattern is common: local for the day's work under close supervision, cloud for the errands that can fly alone. ## 3. Manage the queue like a coordinator With three agents running, your role changes name: you no longer code, you coordinate. The day organizes differently: morning starts by launching the batch (each task with a clear description and its done criterion), the day passes with corner-of-the-eye vigilance (Conductor's panel or the cloud list says who advances and who stalled) and reviews happen in rounds as tasks return. Two coordinator habits that pay: unstick fast (an agent stuck on a question blocks its whole branch, and answering it takes priority over launching more) and note the patterns (the task that went wrong teaches how to describe the next one better). The new sensation, which the community describes identically everywhere: the work feels more like leading a small team than programming. If you enjoy leading, it pays. If you do not, moderate parallelism (two tasks) gives half the benefit without changing crafts. ## 4. Respect the real limit: your review capacity The whole system has one bottleneck and it is you: five agents produce changes faster than a human reviews them with the care the [reviewing AI code](https://serchai.com/en/guides/ai-code-review/) guide describes. Ignoring that limit is how productivity turns into debt: diagonal merges today, production surprises tomorrow. The honest sizing rule: launch in parallel only what you will genuinely review that day. For most people that is two or three simultaneous tasks, not ten. CodeRabbit's automatic filter raises that ceiling by catching the mechanical, and the ceiling still exists. The strategic consequence: parallelism rewards investment in review (automatic filters, solid tests, a mature instructions file) more than investment in more agents. The team that reviews fast and well can afford more parallel: that is the sequence, not the reverse. The whole sector lives in [AI for software development](https://serchai.com/en/ai-for/software-development/). ## Common mistakes Parallelizing tasks that share files. The later merge conflict consumes the saving and adds the risk of resolving it badly. Independence first, parallelism after. Launching more than you can review. It is parallelism's structural error: the unreviewed branch queue grows until merging unread seems reasonable. That is where the debt started. Ignoring the stuck agent. A task blocked on a question is a dead branch accumulating drift from main. Unsticking takes priority over launching. Starting with parallelism before mastering serial. Task description and review problems multiply by the number of agents. First the system with one, then the scale. ## Frequently asked questions ### How many parallel agents are reasonable? As many as you can review carefully that same day: for most people, two or three. The number rises with automatic filters and solid tests, and falls with large or critical tasks. ### Conductor or Codex's cloud agents? Local with close supervision versus remote without occupying your machine. Conductor if you live in Claude Code and want progress up close. The cloud for independent errands and launching from anywhere. The hybrid is the most common pattern. ### Does parallelism consume more quota? It consumes the same quota faster: three parallel tasks spend like three serial ones, concentrated into fewer hours. Under intensive use, it is the typical push toward the higher plans. ### Is it for teams or only individuals? It scales better in teams: the task queue distributes across coordinators and review spreads out. The limit rule holds, only the review ceiling becomes the whole team's.