so the goal of hra is to be "the tokenmaxxing metaharness for codex"
like this is the goal. multiple codex subscriptions, parallel codex sessions, harnessed to be as autonomous and token efficient as possible. we have the building blocks here, with our RLM-style subagent control, but we need to go deeper here, dispatching subagents with smaller models appropriately, but optimizing for speed and quality over cost, though cost is also important we just need to be clever
assume we are always using certain levels of gpt sol
ultra: for implementing a large changeset where parallelism makes sense, or doing wide research
max: the default
luna max: dispatched for clearly defined or trivial tasks
and we switch on fast mode sparingly where it would make a big difference (like the bottleneck is thinking, or writing a lot of files, where faster inference would actually make a difference)
remember that prompt caching is important, and continuity of conversation is useful for better agent performance, so keep these in mind as you consider how context is given to subagents
this will take some careful planning, as well as introspection into how you (codex) yourself work. there is an open source codex cli which may be valuable to clone and inspect for research purposes.
make a proposal, review it and think about it carefully, revise it, and then go ahead and implement it, i trust your judgment