Agent swarms are groups of AI agents that work on pieces of one goal at the same time, then hand their results back to be combined.
Also known as Multi-agent swarms, agent teams, parallel agents
An agent swarm is a setup where many AI agents run in parallel instead of one agent working through a list. Each agent gets a narrow job, such as pulling competitor ads, drafting headline variants, or checking a file against brand rules, and a coordinating layer merges the results.
The idea moved into the mainstream this fall as consumer and enterprise assistants began running teams of agents across thousands of connected apps on a single request. The practical shift is speed and breadth: work that took one agent an hour can be split into dozens of small tasks that finish together.
Swarms change how agency work is divided, and who or what does each piece.
Production gets parallel. A single brief can fan out into copy, resizing, QA and trafficking tasks that all run at once, which compresses turnaround on versioning-heavy work.
Oversight gets harder. More agents means more places for an error to hide. Someone has to own review, logging and the final sign-off before anything reaches a client.
Costs scale with the swarm. Every agent burns tokens. Agencies need to price swarm-driven work knowing that cheap individual tasks add up quickly.
A paid social team gets a 40-asset holiday refresh for a retail client. Instead of one person resizing and rewriting in sequence, a coordinator agent splits the job: one agent adapts copy for each placement, another checks every asset against the client’s legal disclaimers, a third builds the naming sheet for trafficking. A producer reviews the merged package once, fixes two flagged disclaimers, and ships it the same afternoon.
The automations and agents module of the workshop teaches you how to build AI workflows that compress the busywork without taking the craft out of the studio.