Describe a problem you actually have. A supervisor agent will restate it, decide what needs investigating, dispatch three specialists concurrently, then fold their findings into a scoping brief. This is the same first move as a real engagement — run live, not pre-recorded.
Every run executes a LangGraph StateGraph on our server: one supervisor call, three specialist calls dispatched concurrently, and a synthesis call. Nothing here is scripted or cached, which is also why it takes a few seconds.
One prompt asked to do everything produces vague answers. Splitting the work gives each agent a narrow remit and a specific question, and forces the disagreements between them into the open where you can see them.
The specialists do not depend on each other, so they run in parallel and the round costs one call's latency instead of three. Where steps genuinely depend on each other, that option disappears — which is exactly the trade-off graph design is for.
No prices, no timelines, no promised accuracy. It reasons from one paragraph with no access to your data, so treat it as a structured first pass — the useful part is the questions it raises, not the answers.
With your data, your constraints, and a target worth measuring.