Multi-agent architectures are having a moment, and for good reason — they let you decompose a complex workflow into specialized roles, each with its own tools, context and evaluation criteria. But they also add coordination overhead, latency and debugging complexity that a single well-scoped agent doesn’t have.
When a single agent is the right call
If the task has one clear goal, one primary tool surface, and a bounded set of decisions, a single agent with a well-designed system prompt and tool set will usually outperform a multi-agent setup on both cost and reliability. Most customer support and internal Q&A use cases fall here.
When multi-agent architecture earns its complexity
Multi-agent design pays off when distinct sub-tasks genuinely benefit from different context windows, tools or even different underlying models — for example, a “research” agent that reads broadly and a “drafting” agent that writes concisely, coordinated by an orchestrator that manages state between them.
Our supply-chain copilot work is a good example: a monitoring agent, a prediction agent and a communications agent each do one thing well, and a shared task queue keeps them coordinated without any one agent needing the full context of the others. That separation made the system far easier to test and to explain to a skeptical operations team than one giant agent would have been.
The practical takeaway: start with the simplest architecture that could plausibly work, prove it in production, and only add agents when a specific bottleneck demands it.