What Does It Mean to Combine Multiple AI Agents?
In the previous chapter, I defined an AI agent as "a single unit of AI that receives instructions, builds its own sequence of steps, and carries out multiple tasks in order."
This chapter looks at what happens when you have more than one of those units working together.
What one agent can do, and what gets harder alone
It is technically possible to ask a single agent to "write an article, check it, revise it, and then decide whether to publish it."
But in practice, problems come up.
When the same agent that wrote the content also checks it, it cannot step outside its own perspective. It carries the same assumptions it had while writing, which makes it easier to miss gaps or errors. And when "creating" and "deciding" happen inside the same unit, it becomes hard to trace where a particular decision was actually made.
The same thing happens with human work. When you proofread your own writing, you tend to read past your own mistakes. Doing everything alone trades efficiency for a higher risk of overlooking things.
The structure is the same for AI agents.
The idea of dividing roles and passing work along
Combining multiple agents is not simply a matter of "adding more." It means splitting roles, and passing each agent's output (the result of its work) to the next agent in sequence, until the job is finished.
At a basic level, the structure looks like this.
- An agent assigned to "produce a draft" generates the initial text
- An agent assigned to "adjust the tone (writing style and register)" receives that draft and refines it
- An agent assigned to "check the content" identifies any issues
- Finally, a person or a separate agent reviews and gives approval
Each agent focuses on its own assigned area. The agent responsible for writing thinks only about writing. The agent responsible for checking thinks only about whether the content is correct and complete. Because the roles are separate, each agent works from its own point of view.
This blog itself runs on this kind of structure. An AI handles the draft, a separate AI handles tone and style, and another AI checks the quality of the content in sequence before a single article is finished. I will leave out the specific role names and system names, but this article went through that same process to get here.
The safety and quality that come from combining roles
Here is a more concrete look at what changes when roles are divided.
The first is independent checking. Because the agent that writes and the agent that checks are different, the assumptions of the writer are less likely to pass through unchallenged. A separate set of eyes finds problems more easily than reviewing your own work. This is simple, but the effect is not small.
The second is making the path visible. There is a record of "which agent produced which output and what it passed to the next agent." When something goes wrong, you can look back and see where it happened. When everything is left to a single agent, that path becomes hard to see.
The third is clear ownership of each stage. When something does not work, you can address it by asking "what would the agent responsible for this stage need to change?" When everything is packed into one unit, it becomes hard to pinpoint where to fix things.
These three ideas also appear in how human organizations are designed. That said, what I am discussing here is not how companies are organized, but how roles are divided among AI agents. It is not the same as human organizations, but the underlying structure, that dividing roles makes things clearer, is shared.
What this series calls "AI organization"
Combining multiple agents means doing three things.
- Placing agents with different roles alongside each other
- Passing each agent's output to the next in sequence
- Using that process to complete a single piece of work
This is the basic structure of what this series calls "AI organization" (using multiple AI agents with distinct roles to carry out a task together).
A single agent has limits when it comes to quality and safety. Combining agents makes it easier to get closer to both. Things will not become perfect, but "who did what" and "where was each decision made" become more visible. That is the starting point for this approach.
There is more to say about how a single agent and multiple agents differ in practice. This chapter covered what is happening at a structural level, but that question continues into the next chapter.