I feel like most AI workflows can be solved pretty effectively by a single capable LLM or with upto 5 subagents however, many engineering teams are focused on multi-agent architectures at huge scales.

Curious to understand exactly when it becomes worth it / what production use cases there are for large multi-agent swarms: I’m trying to understand exactly where that value lies.

If you are run agent swarms in production: what is the main use case / need and what is your biggest pain point right now (state sync, token costs, cascading failures, latency)?

[As context: I am a founder at Acyclic Labs (YC F26) and we are building infra to scale agents. Looking to map out when the swarms are actually justified and when they are wasteful!]

I’d love examples of it actually working but right now all it’s seemed to be is hype.

most of the pain at scale isn't the agents themselves, it's observability. once you're past a handful you basically need per-agent tracing or you're debugging blind, worth borrowing tooling from how people do this for microservices