Agent swarms and the new model economics
New research into 'agent swarms' demonstrates that coordinating multiple AI models to work toward a single goal can improve performance on complex tasks like software development. The study suggests that tree-like task decomposition and flexible orchestration allow for more efficient scaling of compute and context.
Why it matters
This approach to AI agent architecture could significantly lower the cost and increase the capability of automated systems performing complex engineering tasks.
Earlier 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.
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