Should you build a single agent or a multi-agent system?
Start with a single agent. One agent with a clear set of tools is easier to build, debug, and trust, and it handles most tasks. Reach for multiple agents only when the work splits into distinct specialties that benefit from separate context and instructions — and accept that you're trading raw capability for new failure modes: coordination overhead, agents talking past each other, and harder debugging. Multi-agent is a way to manage complexity, not a free upgrade.
Default to a single agent
A single agent — one model, one prompt, one toolset, one loop — is the simplest thing that can work, and it covers a surprising amount of ground. You can read its trace top to bottom, see why it did what it did, and fix it. Most teams reach for multiple agents before they’ve exhausted what one well-built agent can do.
When multiple agents earn their place
The case for splitting up is real when a task breaks into distinct specialties — a researcher, a coder, a reviewer — each needing its own instructions, tools, and context window that would clutter a single prompt. Separating them keeps each agent focused and lets you scale or swap pieces independently. The win is managed complexity and cleaner division of labor.
The costs you take on
Every agent you add is another thing that can fail, and the connections between them are where multi-agent systems break: handoffs drop context, agents loop or contradict each other, and a small fault in one cascades. Debugging gets harder because the failure is now spread across a conversation between components. Add agents when the problem demands it, not because more sounds more capable.
From the conversation
This explainer is drawn from these episodes — each carries its full transcript.
