I work across multiple models and harnesses using a really simple redis-backed message bus called "nova-bus": https://github.com/mas-bandwidth/nova-tools (open source).
With just this message bus, I can coordinate AIs to work together at a normal pace (worktrees, subtasks etc...), but past a certain point the AIs can't keep up and I need to use a sprint card system I developed to coordinate work.
With this sprint system I'm able to work 200 cards wide (each card is around 30-60 minutes of LLM work in a session) across a fleet of 20 machines linked together with tailscale and ssh.
Full system is here: https://github.com/mas-bandwidth/nova-sprint (open source)
It's been very effective at getting high throughput, but afterwards I find that the resulting code can be a bit of a pastiche from different models working in isolation, thus a good pass over working directly with models to clean up and harden the code is necessary.
I find that using nova-bus to coordinate multiple model/harness review of codebase in very effective at increasing code quality. Each model sees things the others don't.
I break work up into four loose tiers:
1. flash - deepseek flash 4.1 / luna / haiku
2. pro - mercury 3 / terra / sonnet
3. heavy - opus 5.5 / sol
4. frontier - fable 5.1 / astra / argon
The decision of how to classify each piece of work is done by the coordinator (typically Opus 5.5...). If a card fails at a particular tier, it's going to get escalated up tiers until it gets sorted.
Each card has multiple readers (code review). I find mercury is very good at reading quickly. Deepseek as well. I will be honest, I think for 99% of stuff I need to do Opus 5.5 @ medium is ideal. I just use the additional models for different points of view, especially the absolute frontier models fable, astra, argon at max effort for security audits and code review.
I have one agent and already find it quite intense to outsource code writing and basically just make and document decisions.
I feel like I could be rich working like you, I also feel like it is impossible for me to meet the needs of my clients if I'd work like this (so I can't be rich, it's a paradox). I wouldn't be able to answer their questions (without the active agent on-call, context window fully loaded), I wouldn't be able to explain just where the data model needs changing, or why some role really can't exist they way they envision etc, etc, etc.
I could of course branch the whole project and basically discuss multiple implementations during stakeholder meetings, but then still I'd need to explain the advantages/disadvantage of choosing one system over the other and guide them through it.
For example, my work on schema, my protobufs/flatbuffers language replacement for multiplayer games, parallelizes very nicely across 15 languages on the sprint system. https://github.com/mas-bandwidth/schema.
See roadmap here: https://github.com/mas-bandwidth/schema/blob/main/ROADMAP.md
Very difficult to coordinate this amount of work in || without some sort of structured work system.
Even more AI coding work here if you are curious: https://patreon.com/masbandwidth
I did some prototype games, one thing that worked really well for me was having a deterministic replay system where the agents could drive the gamestate via recorded inputs. Kept me from having to do any playtesting or debugging in the main agentic loop.
https://github.com/mas-bandwidth/fixed3d -- port of box3d to fixed point :)
https://github.com/mas-bandwidth/fixed -- fixed point math library
I do you think now need to watch out for the opposite problem. These things become so easy to build that the temptation is just to go ahead and build. But unless you really do need them, all that new code just goes to waste. The cost to produce has become very low, but sometimes the value can end up being negative.