Parallel coding agents: Claude Code and Codex together

Parallel coding agents means running more than one AI coding agent on the same project at the same time. One agent writes a feature, another writes the tests, a third updates the docs. Set up well, you split work you used to do one step at a time. Set up badly, agents overwrite the same files, context bloats and you lose track of who did what.

This post walks through a practical way to run Claude Code and Codex side by side: how to split the work, how to avoid file conflicts and how to move output from one agent to the next.

Why parallel coding agents?

The bottleneck with a single agent is waiting. While it thinks, reads files and runs tests, you either wait or switch to something else. Parallel agents put that waiting time to use.

They pay off most when you have:

• Independent work: a backend endpoint and a frontend form that don’t touch each other.
• Writing and checking: one agent writes the code, another writes tests for the same change.
• Research and implementation: one agent compares library options while another edits existing code.
• Different strengths: Claude Code and Codex ship with different models and tool sets; trying both approaches on the same problem can get you there faster.

Three rules for splitting the work

Whether parallel work succeeds depends on how you split it.

1. Draw file boundaries up front

Two agents editing the same file is the most common failure. Write in each agent’s role which folders it may touch. For example, one agent works only under src/api/, the other only under tests/.

2. Give each agent one goal

Instead of “improve the auth module”, give a measurable goal such as “move session handling to refresh tokens”. A clear goal lets the agent stop early and makes its output easy to check.

3. Decide where the output goes

If one agent’s result feeds another, design that up front. Will it write a plan file, commit, or send its reply straight to the next agent?

Prevent file conflicts with git worktrees

When folder boundaries aren’t enough, the most robust option is a separate working tree per agent. Git worktrees open separate folders on different branches of the same repository:

git worktree add ../acme-web-auth -b auth-refactor
git worktree add ../acme-web-tests -b auth-tests

Each agent works in its own folder and never touches the other’s files. When they’re done, you merge the branches through a normal pull request. I cover this in detail in git worktrees for parallel agents.

In AgentVera, worktrees live in the right panel: create one from a new or existing branch, see its change count and how far it is ahead of or behind the main branch, and start an agent directly in it.

Example setup: Claude Code writes, Codex tests

A simple, effective starting layout:

Setup steps:

1. Add the project folder to AgentVera.
2. In the New agent dialog, pick Claude Code, name it “Architect” and write the file boundaries in its role.
3. Do the same for Codex as “Tester”.
4. Start the dev server in a Terminal agent.
5. Give Architect its task; when it finishes its turn, hand its output to Tester.

Instead of doing the last step by hand, build a flow. Flows send one agent’s last reply to another through a {{output}} template, and wait for the target agent to finish its turn if it’s busy.

Keeping an eye on parallel agents

As the number of agents grows, the real work becomes following them. Watch for:

• Status: which agent is working, which is waiting for you, which hit an error?
• Context size: long conversations cost more tokens on every turn.
• Approval requests: an agent waiting for permission stops while the others keep going.

AgentVera’s workspace shows agents side by side in a grid. Each pane has its status, live context size and a message box. Agents waiting for approval are listed in the status bar, and ⇧⌘A jumps to the one that has waited longest.

Context and cost

Working in parallel also multiplies token usage. Each agent’s first request carries tool definitions, the system prompt and plugins. In long conversations that baseline is re-read on every turn.

To keep cost in check:

• Start agents for simple jobs with a narrower tool set.
• Compact long conversations with /compact.
• When passing output between agents, send only the part that’s needed.

For measured numbers, see cutting Claude Code token usage.

Common mistakes

Two agents on one file. The fastest fix is a worktree or a strict folder boundary.

Vague tasks. Open-ended tasks like “fix the code” make the agent read endless files and bloat the context.

Merging without checking. Run the tests and review the diffs before merging what parallel agents produced. Automatic code review saves time here.

Starting everything at once. Begin with two agents and add a third once the split works.

An end-to-end example: moving to refresh tokens

Splitting work looks easy on paper; here’s how it plays out on a real task. The goal: move a web app’s session handling to short-lived access tokens with rotating refresh tokens.

Splitting the task

First, break the work into independent pieces. This task has three:

• Server side: token issuing, validation and rotation (src/auth/).
• Tests: integration tests for the new flow (tests/api/).
• Docs: reflecting the API changes in the README.

Server side and tests look at the same behavior but live in different folders. Docs only make sense once the server side is settled. So run the first two in parallel and the third afterwards.

Role instructions

Keep each agent’s role short and strict. For Architect:

Work only under src/auth/. Don't change tests.
Run pnpm test auth after every change.
When you're done, summarize your changes as a bullet list.

For Tester:

Work only under tests/. Don't touch application code.
Write integration tests for the behaviors in the summary you receive.

The last line of Architect’s role matters: its summary becomes Tester’s input through the flow. The tidier the summary, the fewer files Tester has to read.

Handoff and follow-up

When Architect finishes its turn, the flow sends its last reply to Tester through a template. If Tester is busy, the flow waits for its turn to end, and the connection’s turn limit stops the two agents from looping forever. Meanwhile you can watch the dev server logs in the terminal agent or have a third agent prepare the docs.

When not to go parallel

Not every job parallelizes. Stick to one agent working step by step when:

• The change is concentrated in one file or a few tightly coupled files.
• The task isn’t clear yet; research and a plan come first.
• One agent’s output is a precondition for the other, and the second job is short.

In those cases a staged pipeline works better than parallelism. I cover that in AI agent workflow.

Checklist

Before a parallel session:

• Is each agent’s goal written in one sentence?
• Are file boundaries in the role?
• If conflicts are likely, does each agent have its own worktree?
• If one agent’s output feeds another, is the flow or plan file ready?
• Is there an agent or terminal to run the tests?

Wrapping up

Parallel coding agents save real time on work that splits cleanly. The key is to separate agents and define the handoff between them. Worktrees separate files, flows carry output, and well-written roles separate responsibilities.

AgentVera is built to set this up in one window: Claude Code, Codex and other CLIs run side by side with their own logins. You can start on the free plan and check the plans when you need more.