swarm-safety

You can simulate multi-agent AI systems by defining scenarios via YAML with diverse agent behaviors like deception and collusion, then analyzing emergent risks using continuous metrics. The skill runs complex interactions to test governance mechanisms against adaptive adversaries.

Distributional safety in multi-agent simulations measures systemic risks using soft probabilistic labels and continuous metrics like toxicity, quality gap, and incoherence. This approach detects subtle failure modes that binary safety classifications often miss in complex agent interactions.

To test AI governance mechanisms, configure scenarios with honest, deceptive, and adversarial agent types, then apply governance interventions like taxes, audits, or circuit breakers. The simulation evaluates how these strategies perform against adaptive adversaries in a marketplace environment.

Yes, LangGraph, CrewAI, and LangChain integrations are supported as underlying frameworks for orchestrating multi-agent simulations. These dependencies facilitate complex agent interactions while the skill evaluates safety through distributional metrics and governance testing.

Yes, simulation results can be analyzed using Pandas data structures and visualized with Matplotlib, Seaborn, and Plotly. The skill generates continuous metrics and probabilistic labels that integrate directly with these libraries for downstream analysis of emergent risks.

The best way to identify critical safety thresholds is running simulations with adaptive adversaries under different governance strategies, then analyzing continuous metrics like toxicity and incoherence. This reveals failure points and governance efficacy before actual deployment.