Research and comparison work does not get faster when you queue it. It gets faster when you split independent checks and run them at the same time, then merge.
We dispatch sub-agents in parallel. Each run is a separate model call with its own context. One slow or failing run must not block unrelated work. In code, that maps to the same Promise.allSettled pattern every senior engineer knows from Web APIs. Promise.all is the stricter variant when you want the whole group to fail together; we use the primitive that matches the product goal.
The coordination story still tracks Building Effective AI Agents: a lead defines work units, workers execute, and a later step integrates outputs.
So what
Parallelism is not a feature bullet. It is a latency and resilience choice. The buyer question is whether failures are contained and visible, not whether the slide says "concurrent."