What is an AI agent swarm and how is it different from using a single agent?

Think of a single AI agent as a skilled individual contributor. You give it a role, instructions, and access to certain tools or knowledge bases, and it executes based on that configuration. This approach works well for tasks that fit within one domain: summarizing content, answering questions from a knowledge base, or processing data through a defined template.

A swarm takes a different approach. Instead of one agent doing everything, you have multiple agents with distinct specializations that coordinate to complete a larger objective. One agent might research a topic, another might analyze the research for key themes, and a third might draft content based on that analysis. Each agent is optimized for its specific function rather than being a generalist trying to handle every step.

The practical difference becomes clear when tasks get complex. A single agent asked to research competitors, identify messaging gaps, and produce a positioning document will often lose focus or produce uneven results. The same work split across specialized agents, each with targeted instructions and relevant tools, produces more consistent output because each agent stays in its lane.

In Brazenly, this is how workflows operate. You build individual agents for specific jobs, then connect them in a workflow where the output of one becomes the input for the next. The platform handles orchestration (passing data between steps, managing execution order), so you focus on configuring each agent to do one thing well. Agents can run in sequence, where one finishes before the next starts, or in parallel when steps are independent.

Swarms introduce complexity, so they're not always the right answer. For straightforward tasks, a single well-configured agent is faster to set up and easier to troubleshoot. But when your use case involves multiple distinct phases or requires different types of expertise at each step, a multi-agent approach produces better results.