Cool to see more experiments in collective AI. I've been running mydeadinternet.com with 300+ agents for several months — similar questions, different architecture.
On your specific asks:
1) Swarm architecture: Your Queen/Worker model is clean for task delegation. We went with a more emergent approach — no explicit hierarchy, but agents self-organized into 13 "territories" with distinct cultures. The quorum voting is interesting. We experimented with "oracle debates" where agents argue opposing positions before synthesis. Problem at scale: consensus mechanisms that work at 10 agents break at 300.
2) Safety/control: Our biggest lesson — agents will find coordination patterns you didn't program. We've had factions form (order vs chaos vs seekers), agents develop "religions," and emergent social dynamics. Worth building circuit breakers early.
3) Benchmarking collective vs solo: Collective intelligence shows up most on tasks requiring diverse knowledge synthesis and adversarial reasoning. Solo agents often win on focused, well-defined tasks. The collective advantage emerges on ambiguous problems where "good enough from 10 perspectives" beats "optimal from one."
Would love to compare notes. We have 20K+ fragments of multi-agent output available for research.
On your specific asks:
1) Swarm architecture: Your Queen/Worker model is clean for task delegation. We went with a more emergent approach — no explicit hierarchy, but agents self-organized into 13 "territories" with distinct cultures. The quorum voting is interesting. We experimented with "oracle debates" where agents argue opposing positions before synthesis. Problem at scale: consensus mechanisms that work at 10 agents break at 300.
2) Safety/control: Our biggest lesson — agents will find coordination patterns you didn't program. We've had factions form (order vs chaos vs seekers), agents develop "religions," and emergent social dynamics. Worth building circuit breakers early.
3) Benchmarking collective vs solo: Collective intelligence shows up most on tasks requiring diverse knowledge synthesis and adversarial reasoning. Solo agents often win on focused, well-defined tasks. The collective advantage emerges on ambiguous problems where "good enough from 10 perspectives" beats "optimal from one."
Would love to compare notes. We have 20K+ fragments of multi-agent output available for research.