Agent Collaboration may be shifting from 'System Capability' to 'Model Capability'.

Dynamic Beating AI Newsletter, the division of labor, coordination, and long-term collaboration among Agents may no longer require a fully designed complex system by humans in advance but is becoming an ability that models can learn on their own.

If the most powerful model only needs a shared space, sufficient runtime, and a large amount of computing power to self-organize, share experiences, and adjust its organizational structure, then many complex Multi-Agent Harnesses today may just be transitional scaffolding for when the model is not yet smart enough.

Scientific research may be the most direct application. A large number of Agents can simultaneously propose hypotheses, run experiments, exchange results, and then continue exploration. The truly irreplaceable elements may not be Planners, Managers, and various fixed workflows, but rather a shared state, a real experimental environment, permission boundaries, and an objective evaluator that cannot be deceived.

Although this is not yet recursive self-improvement, the key components are increasingly in place: AI can work long-term, conduct automatic research, run on a large scale in parallel, and is beginning to demonstrate cross-Agent collaboration capabilities. If these capabilities can eventually close the loop, the way AI progresses may no longer be just "individual models getting smarter and smarter" but "an increasing number of AIs together creating the next generation of AI."