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[return to "Towards a science of scaling agent systems: When and why agent systems work"]
1. Falimo+PP[view] [source] 2026-02-02 01:05:43
>>gmays+(OP)
I've been building something in this space ("Clink" - multi-agent coordination layer) and this research confirms some of the assumptions that motivated the project. You can't just throw more agents at a problem and expect it to get better.

The error amplification numbers are wild! 17x for independent agents vs 4x with some central coordination. Clink provides users (and more importantly their agents) the primitives to choose their own pattern.

The most relevant features are...

- work queues with claim/release for parallelizable tasks - checkpoint dependencies when things need to be sequential - consensus voting as a gate before anything critical happens

The part about tool count increasing coordination overhead is interesting too. I've been considering exposing just a single tool to address this, but I wonder how this plays out as people start stacking more MCP servers together. It feels like we're all still learning what works here. The docs are at https://docs.clink.voxos.ai if anyone wants to poke around!

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2. storus+o71[view] [source] 2026-02-02 04:02:55
>>Falimo+PP
Wouldn't it be better just to stack functionalities of multiple agents into a single agent instead of getting this multi-agent overhead/failure? Many people in academia consider multi-agentic systems to be just an artifact of the current crop of LLMs but with longer and longer reliable context and more reliable calls of larger numbers of tools in recent models multi-agentic systems seem less and less necessary.
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3. Falimo+fS1[view] [source] 2026-02-02 12:24:48
>>storus+o71
In some cases, you might actually want to cleanly separate parallel agents' context, no? I suppose you could make your main agent with stack functionalities responsible for limiting the prompt of any subagents it spawns.

My hunch is that we'll see a number of workflows that will benefit from this type of distributed system. Namely, ones that involve agents having to collaborate across timezones and interact with humans from different departments at large organizations.

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