The economics of agent swarms aren't obvious. More agents doesn't automatically mean better outcomes or lower spend. It depends on how tightly you've defined the problem.
I've seen teams spin up swarms for tasks that a single well-scoped agent handles faster and cheaper. Here's what actually matters:
Problem decomposition has to be real. If your subtasks genuinely run in parallel and don't require constant synchronization, swarms win. If they're just sequential steps disguised as agents, you're paying for coordination overhead instead of saving it.
State handoff is expensive. Every time one agent passes work to another, you're serializing context, adding latency, and risking information loss. The fewer handoffs, the better. A swarm makes sense when each agent can operate independently on a clearly isolated piece of the problem.
Failure modes compound. One agent failing is one failure. Five agents with interdependencies means debugging cascades. If you need audit trails and trustworthiness (compliance, client review, recovery), swarms add complexity that has to be accounted for in your delivery timeline.
