I am inspired by this to make a harness that intentionally does sparse checkouts, so the agents only can see so much context.

I am also playing with the idea of layering additional data structures on top of the "code" that cover different aspects. For example, a tree structure of performance requirements that has durable symbolic references to entities in the code that are especially relevant. Another tree can cover aspects of security, another legal compliance, another UI guidelines.

Another kind of review not mentioned here or in Anthropic's article is the plan review. I generate the plan for some stretch of work, then have the plan (saved as a file) reviewed by one or more agents. This checks the plan against project constraints before executing it.

If we can solve testing then coding is almost fully automated. The only remaining problem was always trust, why should we trust that code? If you read it manually then it's like walking a motorcycle. If you don't, then you need some way to derisk it. There are many ideas flowing around about how to do this, the OP and Anthropic showed their takes. I think the approach is project specific though, no general method.

I'm aware that GH recently released stacked PRs, but it still seems like a long way to go both from usability and reliability perspective compared to what Meta and likely Anthropic have internally.