Comment by daxfohl
14 hours ago
True, though even then I kind of wonder what's the point. Once they build an AI that's as good as a human coder but 1000x faster, parallelization no longer buys you anything. Writing and deploying the code is no longer the bottleneck, so the extra coordination required for parallelism seems like extra cost and risk with no practical benefit.
Each agent having their own fresh context window for each task is probably alone a good way to improve quality. And then I can imagine agents reviewing each others work might work to improve quality as well, like how GPT-5 Pro improves upon GPT-5 Thinking.
There's no need to anthropomorphize though. One loop that maintains some state and various context trees gets you all that in a more controlled fashion, and you can do things like cache KV caches across sessions, roll back a session globally, use different models for different tasks, etc. Assuming a one-to-one-to-one relationship between loops and LLM and context sounds cooler--distributed independent agents--but ultimately that approach just limits what you can do and makes coordination a lot harder, for very little realizable gain.
The solutions you suggest are multiple agents. An agent is nothing more than a linear context and a system that calls tools in a loop while appending to that context. Whether you run them in a single thread where you fork the context and hotswap between the branches, or multiple threads where each thread keeps track of its own context, you are running multiple agents either way.
Fundamentally, forking your context, or rolling back your context, or whatever else you want to do to your context also has coordination costs. The models still have to decide when to take those actions unless you are doing it manually, in which case you haven't really solved the context problems, you've just given them to the human in the loop.
It’s more about context management, not speed
Do you really need a full dev team ensemble to manage context? Surely subagents are enough.
Potato, potatoh. People get confused by all this agent talk and forget that, at the end of the day, LLM calls are effectively stateless. It's all abstractions around how to manage the context you send with each request.