Comment by sothatsit
11 hours ago
They haven’t released this feature, so maybe they know the models aren’t good enough yet.
I also think it’s interesting to see Anthropic continue to experiment at the edge of what models are capable of, and having it in the harness will probably let them fine-tune for it. It may not work today, but it might work at the end of 2026.
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.
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It’s more about context management, not speed
Do you really need a full dev team ensemble to manage context? Surely subagents are enough.
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