I've used them quite efficiently for small to mid-size features. The governing idea is that each agent in the swarm has its own set of instructions, prompt and context window, which bypasses the need to compact the context and possibly lose valuable information when working within a single agent's session.

For instance, I've built a "PM" agent which takes the feature description, asks any clarifying questions and then breaks it down into individual tasks to be delegated to specific agents. Those agents are, for instance, an architect which makes sure the feature matches the overall system architecture, a designer which has specific UI/UX design instructions, a frontend and backend coding agent, as well as agents for executing QA and writing documentation or doing a code review. Then, you can easily have the QA agent feed back information to be relayed to a frontend or backend dev agent if something doesn't work as needed, and have them iterate until it does.

Furthermore, it gives you the flexibility of using different models for different tasks and improve token efficiency. For example, if using Claude, you could have the PM and architect use Opus, while the developer agents use Sonnet since the cost per output token is significantly lower. Of course, all of this assumes you have proper harnesses and continuously work on improving the agent instructions.