One job.
Many minds.
At once.
Agents takes a big task, splits it into parts that can stand on their own, runs them across a team of AI workers at the same time, and gathers the results back into one place.
How a job moves through it
Split it, run it all at once, gather it back.
Split
A large task is divided into parts that don't depend on each other — each with a clear, scoped assignment of its own.
Run in parallel
Every part is handed to its own agent and worked at the same time. Instead of one assistant plodding step by step, the whole team moves together.
Report back
Each worker returns a structured result. The pieces are collected into one place — ready to combine, review, or feed into whatever comes next.
What you get
A coordinator, not just a chatbot.
Parallel fan-out
One request becomes many. Independent parts are dispatched to run simultaneously — not one after another — so wall-clock time drops with the count.
Scoped delegation
Each agent gets a narrow assignment and works in isolation. The slices stay separate, so workers don't collide or overwrite each other's progress.
Structured results
Findings come back in a consistent, machine-readable shape — easy to gather, compare, and hand off, instead of a wall of free-form prose per worker.
Coordinated by design
The split, the dispatch, and the gather are the tool's job. You describe the work once; the orchestration of who does what, in parallel, is handled for you.
Fewer round-trips. More done at once.
When a problem breaks cleanly into parts, running them together beats working through them in sequence. That's the whole idea.