I don't think number of parallel agents is the right productivity metric, or at least you need to account for agent efficiency.
replyImagine a superhuman agent who does not need to run in endless loops. It could generate 100k line code-base in a few minutes or solve smaller features in seconds.
In a way, the inefficiency is what leads people to parallelism. There is only room for it because the agents are slow, perhaps the more inefficient and slower the individual agents are, the more parallel we can be.
Few experiments like gas town, the compiler from Anthropic or the browser from Cursor managed to reach the Rocket stage, though in their reports the jagged intelligence of the LLMs was eerily apparent. Do you think we also need better models?
replyI do. The reason why the current generation of agents are good at coding is because the labs have sufficient time and computes to generate synthetic chain-of-thoughts data, feed those data through RL before use them to train the LLMs. These distillation takes time, time which starts from the release of the previous generation of models.
replySo we are just now getting agents which can reliably loop themselves for medium size tasks. This generation opens a new door towards agent-managing-agents chain of thoughts data. I think we would only get multi-agents with high reliability sometimes by the mid to end of 2026, assuming no major geopolitical disruption.