Parallel Subagent Spawning

In a hub-and-spoke multi-agent system, a coordinator decomposes a task, delegates pieces to subagents, then aggregates the results. The key performance lever: multiple Task calls emitted in one response run concurrently.

If a research job needs three independent lookups, you don't have to do them one after another. Spawn all three in a single turn and they execute in parallel, collapsing three round-trips into one wave.

The rule is precise: every Task call the model emits in the same assistant response is dispatched at the same time. There is no special API flag — concurrency is a property of how many Task calls share one response.

• Three Task calls in one response → three subagents run in parallel.
• One Task call, wait for the result, then another Task call next turn → sequential.

So parallelism is a decomposition decision, not a configuration toggle.