Agent swarms and the new model economics
TLDR
Researchers built an agent swarm system that decomposes complex tasks into hierarchical trees with planner agents (using capable models) delegating to worker agents (using cheaper, faster models), then tested it on implementing SQLite in Rust from documentation. The new swarm reached 80% test pass rate in four hours using Grok 4.5, compared to the old system's collapse before hour two on the same task. The system uses specialized coordination mechanisms—custom version control handling 1,000 commits per second, design docs with compile-checked references, third-party merge conflict resolution, and stacked review lenses—enabling productive multi-agent collaboration at scales where single agents drift or human coordination breaks down.
Why it matters
With agent swarms, the unit of work becomes the spec, and this post discusses how to ensure the swarm actually follows the spec as AI capability enables engineers to work at higher levels of abstraction.
