What an anthill can teach us about orchestrating agents.
Findings from ant-sim , a colony simulator I wrote in 2021 and reworked in 2026. Every number below comes from seeded runs on commit d430093 ; pnpm sim:ablation reproduces the table to the digit, and the raw outputs are in docs/results/ . Try the live simulation . Every few months someone rediscovers that ant colonies have no manager and decides our agent systems should work the same way. I…
An anthill offers valuable insights into orchestrating agents, but the analogy has its limitations when applied to software systems. In 2021, the author created a simulator called ant-sim to explore how ant colonies allocate work without a manager and whether the same principles can be applied to software agents. Each ant reads a public board that records the demand and delivery of tasks.
Ants switch tasks based on their own estimate of crowd and the most pressing alternatives, with a threshold for individual response. The colony's behavior emerges from these individual decisions and task couplings. The key to this model is the public board, which makes the state available to all workers. Removing it leads to severe under-serving of foraging tasks and, in some cases, colony starvation.
A crowding estimate, while harder to defend, can reduce under-serving and help colonies recover from shocks. Individual thresholds have little impact on overall metrics. The findings suggest a design hypothesis for agent pools: a board showing current task demand is essential, while a peer-count estimate may help damp herds if sampled properly, and delaying decisions from thin samples can reduce both herd behavior and churn.
However, the simulator does not provide a complete solution, as it does not compare with dispatchers, measured deadlines, or the cost of switching agents.
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