An agentic swarm is a group of AI agents that each take on part of a software task at the same time, coordinated by a lead agent and checked by people.
We work this way on our own platform: one agent writes or checks the specification, several agents build separate parts in parallel, and a person reviews and merges the result.
The fork pattern
A lead agent first checks the specification. It then splits the work into parts that do not depend on each other, such as the interface, the database, sign-in and the API, and hands each part to its own agent.
// Illustration
const swarm = new AgentSwarm();
swarm.fork(4, 'builder');
swarm.assign(['frontend', 'db', 'auth', 'api']);
_ executing...
Where it helps and where it does not
Parallel agents are not faster or better by definition. What we see in practice:
- SpeedParts that do not depend on each other can be built at the same time. Work that does depend on other parts still has to happen in order.
- QualityAgents with different models review each other's code before a person does, which catches different kinds of mistakes. A person stays responsible for what goes live.
- CostAgents are paid per use rather than per hour. What a task costs depends on its size and on how much review it needs.