Agent Swarm

An Agent Swarm isn't a single AI working alone — it's multiple AI agents forming a team that moves in parallel, splitting up one task and handling it together.

An office analogy makes this easy to picture. Instead of one person doing all the research, organizing, and writing themselves, an agent playing the role of "manager" hires as many "employee" agents as needed on the spot, splits them into departments, hands out the work, and then just collects the results. The key point is that this org chart isn't drawn up by a human in advance — the system designs it itself, on the fly, based on what the task requires.

Why go to all this trouble? When a single agent works on something for a long time, its memory capacity fills up, forcing it to summarize and delete earlier content — and in that process, important details get lost. The idea behind Agent Swarm is that if multiple agents each take on short, focused pieces of the work, much larger and more complex tasks can be handled without that kind of loss.

It shows up in phrasing like: "The Agent Swarm built on Kimi K2.5 deploys up to 100 sub-agents at once, executes more than 1,500 tool calls, and delivers results 4.5 times faster than sequential execution."

Contrary to a common misunderstanding, this isn't just about running several AIs at the same time. What Moonshot AI emphasized was that the system itself — not a human — handles the job of organizing the work. The agents decide on their own how many to deploy and how to split up the roles.

If you try the following kinds of requests on a service that supports Agent Swarm, you can get a feel for how multiple sub-agents split up the work.

1. Ask for a broad research task. Example: "Research food trends in 20 different regions of Korea separately and put them into a comparison table."
2. Ask for review from multiple perspectives. Example: "Evaluate this business plan from the perspective of an investor, a lawyer, and a marketer, separately."
3. Once you get the results, compare them with what a single agent produces working through the same task step by step, and see how the speed and breadth of the output differ.