In the end, we already have processes that work for humans and we know the types of testing, verification and review that makes a codebase grow healthily. We just need to adapt these designs the best we can to AIs.

AI have a lot of advantages over humans like speed, controllability, good defaults (e.g. Claude will write helpful comments unprompted which is better than most engineers), and the ability to implement best practices like comprehensive testing with much lower cost than a human team. Using AI in my personal projects, I am able to write software at a much lower bug rate than manually, because now I can make an automated test suite whereas previously I had no time to do so. However, AI also has downsides like rigidity, limited context window, and lack of creativity by default which needs to be resolved with harnessing (e.g. different agents with separate, well-compressed contexts) or worked around with human review layers.

I suspect the best core pattern for coding automation will be to create an engineering manager agent layer that can flexibly apply a set of engineering practices to the concrete problems at hand. It would handle all coding agent pings before it reaches a human, with prompting so that it reliably considers the big picture when making decisions. Very rarely, it would escalate an issue or plan review directly to humans when it is truly blocking, but most likely it would organize the information it obtains from the lower layer coding agents and surface them as reports for humans to review.