Imagine if two powerful AI tools, Claude and Codex, could work together like human programmers. This concept is becoming a reality thanks to research from Cursor. They have developed a multi-agent workflow that mimics human collaboration in coding. In this setup, one agent takes the lead while the other reviews their work. This approach not only speeds up the coding process but also enhances the quality of feedback.

Cursor’s findings show that when Claude and Codex operate as a team, they provide different perspectives on code. Even when they agree, their combined feedback signals strong consensus. This is crucial because it helps teams address all feedback points effectively. The traditional code review process can be slow and noisy, but this new method aims to streamline it.

Building the Loop

To facilitate this interaction, a tool called `loop` was created. It allows users to run Claude and Codex side-by-side in a terminal environment called tmux. This setup enables them to communicate directly with each other while working on code reviews. As a result, the feedback loop becomes faster and more natural.

The `loop` tool keeps context throughout the coding iterations. Users can engage with both agents actively, steering discussions or asking questions as needed. This interactivity makes the agents more proactive in their responses, enhancing overall productivity.

Future of Agentic Workflows

The future of workflows involving multiple agents may resemble familiar teamwork rather than automated processes. As these tech

nologies evolve, they could further improve how we collaborate on software projects. For instance, researchers are exploring ways to make human handoffs easier during code reviews.

Some open questions remain about optimizing this workflow: Should tasks be split across multiple pull requests? Is it beneficial to share planning documents within Git? These considerations could help refine how humans interact with AI during development.

Key takeaways

• Claude and Codex can work together like human programmers.
• The `loop` tool enhances communication between these AIs.
• This new workflow speeds up feedback loops significantly.
• Future developments may further improve collaboration in coding.

A growing number of developers are using multiple agent harnesses for various reasons. Some want to avoid vendor lock-in or maximize their subscriptions while others seek diverse perspectives on coding tasks. Therefore, integrating agent-to-agent communication should be a priority for future multi-agent applications.

If you’re interested in exploring these advancements further, consider checking out NorthNeural. They provide insights into cutting-edge AI technologies that can enhance your workflow.

FAQ

• What is pair programming? Pair programming is a collaborative approach where two programmers work together at one workstation.
• How does `loop` improve coding efficiency? It allows Claude and Codex to communicate directly, speeding up feedback loops during code reviews.
• Why use multiple agent harnesses? Developers use them for diverse perspectives, avoiding vendor lock-in, or maximizing subscription benefits.

Sources

• Original source