GoKawiilEarlier this year, we ran experiments to test the limits of scaling agents to cooperate toward a goal. Our hypothesis was that this would unlock a new tier of task scale and complexity.
The flagship project was a long-running swarm building a web browser from scratch. It succeeded as a proof of concept, but fell far short of polished software.
That work was deliberately empirical. We started from a blank canvas and hill-climbed toward a stable, effective system. Since then, our goal has been to understand the agent swarm well enough to engineer it deliberately.
To test that progress, we returned to a task the old swarm had struggled with: building SQLite from scratch, in Rust, from nothing but its documentation.
Our initial results have been promising. We ran the old and new swarms on the same task, with the same models and the same time budget, and measured how much of a held-out SQL test suite each could pass.
The new swarm did better in every model configuration. Using Grok 4.5, it reached 80% in four hours, while the old swarm spiraled and had to be paused before its second hour.
We also varied which models did which jobs. In some runs, one model handled everything while in others, a frontier model planned while a fast, inexpensive model carried out the work. Every mix produced similar quality, but the costs varied enormously.1
# Trees and leaves
Descriptions of large tasks naturally take the shape of trees, with a goal at the root that subdivides recursively into basic units of work. Our swarm has two roles, both organized around that same tree-like decomposition:
Planner agents, powered by the smartest models, split a goal into pieces and delegate them.
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