Agent-to-Agent Pair Programming

(axeldelafosse.com)

22 points | by axldelafosse 3 hours ago

6 comments

  • bradfox2 51 minutes ago
    Multi turn review of code written by cc reviewed by codex works pretty well. Been one of the only ways to be able to deliver larger scoped features without constant bugs. I've seen them do 10-15 rounds of fix and review until complete.

    Also implemented this as a gh action, works well for sentry to gh to auto triage to fix pr.

    • encoderer 11 minutes ago
      Yes I’ve had a lot of success with this too. I found with prompt tightening I seldom do more than 5 rounds now, but it also does an explicit plan step with plan review.

      Currently I’m authoring with codex and reviewing with opus.

      • axldelafosse 4 minutes ago
        Good reminder: don't forget the plan review!
  • vessenes 1 hour ago
    I prefer claude for generation / creativity, codex for bull-headed, accurate complaining and audit. Very rarely claude just doesn't "get it" and it makes sense to have codex direct edit. But generally I think it's happiest and best used complaining.
  • cadamsdotcom 1 hour ago
    The vibes are great. But there’s a need for more science on this multi agent thing.
    • axldelafosse 12 minutes ago
      I agree! Right now it is leveraging the Codex App Server, which is open-source and very well implemented, but using Claude Code Channels is probably a bit hacky.

      The good thing is that it establishes a direct connection so it's already much better than having one agent spawn the other and wait for its output, or read/write to a shared .md file -- but it would be cool to make it work for all agent harnesses.

      Open to ideas! The repo is open-source.

  • alienreborn 1 hour ago
    I have been trying a similar setup since last week using https://rjcorwin.github.io/cook/
  • jedisct1 1 hour ago
    I systematically use reviewers agents in Swival: https://swival.dev/pages/reviews.html

    Even with the same model (--self-review), that makes a huge difference, and immediately highlights how bad the first iterations of an LLM output can be.