Decomposing into roles

As agent-based systems grow, they tend to accumulate responsibilities. Reasoning, memory management, tool use, coordination, and oversight all compete for space inside a single control loop. This lesson exists to show how we can step back from that pressure and start thinking in terms of roles, which is the first move toward multi-agent design.

The goal here is not implementation. It is learning how to look at a problem and see where one agent’s responsibilities naturally end and another’s should begin.

Identifying distinct responsibilities within a system

Most complex systems are already doing several different kinds of work, even if they are implemented as a single agent.

Some parts of the system decide what should happen next. Other parts focus on how to do a specific piece of work. Still others monitor progress, validate results, or decide when something is finished.

When we identify these responsibilities explicitly, we stop thinking in terms of “the agent” and start thinking in terms of jobs the system needs done. This shift makes it easier to see natural seams where behavior can be separated.

Separating concerns into independent agent roles

Once responsibilities are visible, we can group related ones together. Each group becomes a candidate agent role.

A role should feel cohesive. Its responsibilities should make sense together and not require constant knowledge of unrelated parts of the system. If a responsibility regularly pulls in context that does not belong, it may be a sign the role boundary is wrong.

Separation is about clarity, not isolation. Roles are defined so that each agent can focus on a well-scoped concern without carrying the full mental load of the system.

Assigning clear boundaries to each agent

Agent roles need boundaries to stay useful. A boundary defines what an agent is responsible for and, just as importantly, what it is not responsible for.

Clear boundaries reduce overlap. They prevent two agents from trying to make the same decision or manage the same piece of state. They also make reasoning about behavior easier, because each agent’s actions can be understood in terms of its role alone.

A good boundary makes it obvious when work should be handed off to another agent rather than expanded inside the current one.

Designing agents around capabilities rather than data

It is tempting to design agents around the data they “own.” This often leads to brittle designs where agents exist mainly to guard structures rather than perform meaningful work.

A more durable approach is to design agents around capabilities. An agent is defined by what it can do: planning, transforming input, evaluating results, or executing tasks. Data becomes an input or output of those capabilities, not the reason the agent exists.

This mindset keeps roles stable even as the underlying data changes.

Coordinating agents conceptually without implementation details

At this stage, coordination is a mental model, not a technical one. We care about who depends on whom, not how messages are sent or state is shared.

Conceptual coordination answers questions like: which agent initiates work, which agents respond, and which agent decides what happens next. These relationships can be sketched and reasoned about without choosing protocols, SDK features, or infrastructure.

By keeping coordination abstract, we preserve flexibility. Implementation choices can come later, guided by a clear understanding of roles and responsibilities.

Conclusion

We started with the problem of growing complexity and ended with a way to break that complexity into clear agent roles. By identifying responsibilities, separating concerns, defining boundaries, focusing on capabilities, and reasoning about coordination conceptually, we gain a clean mental framework for multi-agent systems.

At this point, we are oriented. We can look at a system and see where multiple agents would naturally fit, even before writing a single line of code.