Multi-agent system

A multi-agent system is several AI agents working together on one problem, each with its own role, rather than a single agent doing everything. One might research, another write, another review, coordinated by an orchestrator or passing work between them. The idea is the same as a small team: specialists who each do one thing well beat a generalist stretched thin.

For a founder this is how you tackle work too big or too varied for a single prompt, a full content pipeline, a research report, a complex support flow. Splitting roles can lift quality, because a dedicated reviewer agent catches what the writer missed, and lets steps run in parallel. The cost is real, though: more agents mean more tokens, more coordination overhead, and more places for a handoff to drop the ball. Reach for a multi-agent system only when one good agent has genuinely hit its limit, not because it sounds impressive.