We spent a lot of time tweaking skills, doc files, and prompts. I’d say that was our primary activity as engineers. Our job became tweaking the harness every time we got code or results we didn’t like. Eventually we were pretty happy with most agent runs, but we were always happy to just throw out ones that didn’t meet our standards. I think more than half didn’t.
1. Yes! Many teams internally have adopted a lot of the same practices we outlined in the blog post. Ryan has also been spending time both internally and externally helping companies figure out how to do this in their code bases.
2. Hmm, kind of. There have definitely been issues the models can’t one shot. But we still use Codex to write all the actual code with human guidance.
3. More agents :) Some teams are experimenting with centralized Agent mediated integration queues, others use normal merge queues, many have local Codex threads that monitor CI to resolve and land conflicts or failures.
4. Today’s models and codex app. We started doing all this with gpt-5 and codex-cli. The tools today, 9 months later, are so much better than what we had then.
Correct on the first part, partially correct on the second. LOC is a bad metric, but it is at least a legible one. Lots of people working on better ways to measure Software Productivity!
At the time we wrote the article we hadn’t released the product and weren’t ready to talk about it. It was an internal prototype that looked very much like the current Codex app.