What is a Single-Agent vs Multi-Agent Systems?

A single-agent design keeps everything — reasoning, tool use, and context — in one continuous process, which minimizes coordination overhead and keeps the full task history in one place, making it well suited to tasks that benefit from one consistent line of reasoning holding the whole context. The tradeoff is that a single agent's context window and attention get stretched thin on very large, multi-part tasks, and it can't parallelize independent sub-tasks. Multi-agent systems split work across specialized agents — a researcher agent, a coding agent, a review agent — that each hold a narrower context and can run some sub-tasks in parallel, then hand off or reconcile results through an orchestrator. This suits work that decomposes cleanly into independent parts, or where a specialist agent genuinely reasons better with a narrow, focused context than a generalist agent juggling everything at once. The cost is coordination complexity: results have to be merged correctly, and miscommunication between agents can introduce errors a single agent wouldn't make. The practical heuristic mirrors software design generally: choose multi-agent when a task is decomposable, parallelizable, and throughput-bound; choose single-agent when the task is deep, tightly coupled, and benefits from one continuous train of thought holding the whole picture.