Boost Your Coding Efficiency with Claude and Codex
Imagine if two AI tools, Claude and Codex, could work together like human programmers. This idea is becoming a reality thanks to research from Cursor. They have developed a system where these two AIs can communicate directly, enhancing coding workflows significantly.

In this setup, one AI acts as the main worker while the other serves as a reviewer. This mimics how human teams collaborate on projects. The result? Faster feedback loops and improved code quality.
How Claude and Codex Work Together
Cursor’s research led to the creation of a multi-agent workflow. In this model, a main orchestrator assigns tasks to different agents. Claude Code “Agent teams” and Codex’s “Multi-agent” features operate similarly, with subagents reporting back to the main agent.

The Benefits of Using `loop`
To streamline this process further, I created `loop`, a simple command-line interface (CLI). It allows users to run Claude and Codex side-by-side in tmux, enabling them to communicate effectively.
This setup makes it easier for both AIs to provide proactive feedback while maintaining context throughout the coding process. Users can stay engaged in the loop, answer questions, and guide the review as needed.
Challenges in Human Review
While using mult
iple agents offers many advantages, it can complicate human reviews. For instance, when both AIs suggest changes, it may lead to more modifications than expected.
This can be beneficial but also makes it harder for humans to keep track of everything. Questions arise about how best to manage these interactions during pull requests (PRs).
Key Takeaways
- Claude and Codex can collaborate like human programmers.
- The `loop` CLI enhances communication between AIs for better feedback.
- Using multiple agents may complicate human reviews but improves overall efficiency.
- Consider sharing plans or recordings during PRs for clarity.
Many users are adopting multi-agent systems for various reasons. These include avoiding vendor lock-in or maximizing subscription benefits. As a result, agent-to-agent communication should be prioritized in future developments.
A Practical Example
If you’re working on a project that requires frequent code reviews, try implementing `loop`. You might find that having both Claude and Codex provide feedback leads to quicker resolutions of issues.
Next Steps
Frequently Asked Questions
- What is pair programming?
- How does `loop` improve coding efficiency?
- Can I use other AIs in conjunction with Claude and Codex?
For the original report, see the source article.
