Tradeoffs of multi-agent systems

Multi-agent designs appear in this syllabus because they promise a way out of growing complexity. By splitting responsibilities across agents, we can often reason about systems more cleanly. But this shift is not free. Understanding the costs helps us decide when multiple agents help and when they simply add weight.

Increased coordination complexity

When we introduce more than one agent, coordination becomes a first-class concern. Agents must agree on who does what and when work is considered complete. This coordination logic often grows alongside the agents themselves.

Instead of reasoning about a single flow of control, we now reason about interactions. The mental model expands from “what happens next” to “who is responsible for this step.”

Communication overhead between agents

Agents rarely work in isolation. They exchange messages, share artifacts, or update shared state. Each exchange adds overhead, both in time and in design effort.

Even simple communication requires structure. Messages must be formatted, interpreted, and validated, which introduces additional work compared to direct function calls inside a single program.

Debugging and observability challenges

With multiple agents, behavior is distributed. A surprising outcome may result from interactions rather than a single decision. Tracing what happened means reconstructing conversations, task assignments, and intermediate results.

This makes observability more important and more difficult. Logs, traces, and state snapshots become essential just to understand normal behavior.

Performance and latency considerations

Multi-agent systems often involve waiting. One agent may pause while another completes work or responds to a request. These pauses can accumulate, increasing overall latency.

Parallelism can offset this cost, but only when tasks are truly independent. Otherwise, the system may feel slower despite doing the same amount of work.

When multi-agent systems are not worth the cost

Not every problem benefits from decomposition. If responsibilities are tightly coupled or decisions must be made with full global context, splitting into agents can add friction without clarity.

In these cases, a single well-structured agent is often easier to build, reason about, and maintain.

Conclusion

The purpose of this lesson was to make the costs of multi-agent designs visible. We now have a clearer sense of what we trade away in exchange for modularity and separation of concerns. With that perspective, we are better equipped to judge when multiple agents are a sensible architectural move and when restraint leads to simpler, stronger systems.