Agent Swarm

Some AI programs don't just answer questions but carry out multi-step assignments on their own. They search the internet, read files, write text, or launch other programs. Such programs are called agents. An agent swarm is a group of several such agents that work together on the same task. Each one takes on a part, passes along its intermediate result, and receives the results of the others in return. In the end, this produces a joint solution that no single agent delivered on its own.

Why tasks are distributed across many agents

A single agent working on a long task from start to finish easily loses the thread. The more steps it takes, the more intermediate information it has to keep track of at the same time. Small errors from step three get carried along all the way to step twenty. A swarm limits this problem because each agent only handles a manageable slice.

Then there's speed. Ten agents can search ten sources at the same time, while a single one would have to work through them one after another. For research tasks, this significantly shortens waiting time. Companies like OpenAI, Anthropic, and Google advertise their research tools with exactly this benefit.

A third reason is control. One agent can be used solely to check the work of the others. This checker looks for contradictions, missing sources, or obvious nonsense. This doesn't replace human oversight, but it does catch some errors early.

Role distribution, messages, and stop rules

Usually there is one agent that breaks down the task. It is called the orchestrator or planner and distributes subtasks to the others. These worker agents each receive a narrowly defined instruction, such as: check this company's revenue figures for 2024. Their answers come together at the planner, which builds the final result from them. Technically, these are often the same language models, just equipped with different instructions and different tools.

The agents don't really talk to each other like humans. They exchange text through a fixed interface, similar to how programs send each other files. Some systems additionally use a shared memory that all of them can write their results into. This way, each agent can see what the others have already found out.

An important part is the emergency brake. Without rules, agents could keep throwing new tasks at each other and run on endlessly. Developers therefore set upper limits: a maximum number of steps, a maximum number of agents, a maximum amount of compute time. These limits are also a cost issue, since every work step costs money at the model provider.

From deep research to coding assistants

Today, swarms are most commonly encountered in the research features of chatbots. When a tool takes several minutes to produce an answer and then delivers a long report with sources, several agents are often working in parallel behind the scenes. Coding assistants use the same principle: one agent changes the code, another runs the tests, a third describes the change.

In business news, the term usually comes up in connection with costs and data centers. A swarm consumes a multiple of the computing power of a single request. This is one of the reasons why the AI industry's demand for electricity and chips is growing so sharply.

A common misconception is that a swarm is automatically smarter than a single model. That's not true. If all the agents are based on the same model, they also share its blind spots. Fabricated facts from one agent can be picked up and passed along by the others. More agents therefore mean more manpower, but not necessarily more reliability.