The paper sounds too shallow. The errors data doesn't seem to have a rationale or correlation against the architecture. Specifically, what makes the SAS architecture to have lowest error rates while the similar architecture with independent agents having highest error rates? The conclusion doesn't seem well-grounded with reasoning.

> As tasks require more tools (e.g., a coding agent with access to 16+ tools), the "tax" of coordinating multiple agents increases disproportionately.

This aligns well the principle of highly cohesive, loosely coupled design for software components. If you instruct the AI to design this way, it should result in components that're simpler to reason about, and require fewer sequential steps to work on. You can think of cohesion in many different ways, but one is common functions, and another is tool/library dependency.

A single-agent system (SAS) uses this budget for a deep, unified reasoning stream (averaging 7.2 turns), multi-agent teams would fragment the same budget into dozens of coordination messages

I wonder if the budget is increased (say 50k) would the same results be observed ?

Good for promo projects though, lol

Empirically, a top level orchestrator that calls out to a planning committee, then generates a task-dag from the plan which gets orchestrated in parallel where possible is the thing I've seen put in the best results in various heterogeneous environments. As models evolve, crosstalk may become less of a liability.

> Average performance (%) across four agentic benchmarks improves consistently with increasing model Intelligence Index.

> Centralized and hybrid coordination generally yield superior scaling efficiency, suggesting that collaborative agentic structures amplify capability gains more effectively than individual scaling alone.

Then again, the deltas between SAS and best performing MAS approach are ~8%, so I can't help wonder if it's worth the extra cost, at least for the generation of models that was studied.

1: https://github.com/yohey-w/multi-agent-shogun

Is this going to be released for general use?

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!

The rest is trash they are forcing down our throats