Building Hierarchical Agent Structures for Scalability

Picture a group of friends building a robot together. One person builds the frame, another wires the circuits, and a third installs the software. The sequence matters—you can't install the software before the circuits are wired, and you can't wire the circuits without a frame. This is a sequential process, where each step follows the previous one in a specific order. In CrewAI, this sequence is the default behavior for controlling agents and tasks.

Now, imagine creating two agents in CrewAI: a content planner to gather data on the latest YouTube trends and a scriptwriter to craft an engaging video script. The scriptwriter can't start until the content planner has finished their work, just like you can't bake a cake without ingredients. Sequential processes work well when tasks have a clear order, but what happens when tasks are more complex or interdependent? This brings us to a crucial question: Do we always need a hierarchy when working with agents? The short answer is no, but the longer answer is much more interesting—and relevant to what we'll explore next.

What are hierarchical systems?

Imagine managing a research project where you need to gather data, analyze it, and compile a report. Each task is important and interdependent—the quality of your analysis depends on good data, and the clarity of your report depends on solid analysis. If agents worked independently without coordination, the data gathering agent might collect irrelevant information, the analysis agent could miss key trends, and the report might not make sense. Without coordination, things can easily go off track.

This is where hierarchical structures come in multi-agent systems. In a hierarchy, some agents take on the role of supervisors or coordinators. Their job is to ensure that everything fits together nicely, just like a research project manager who keeps the whole team aligned and on target.