swarm

You can measure emergent failures in multi-agent systems by running Python simulations that calculate interaction-level safety metrics like illusion delta and quality gaps to assess systemic risks.

Multi-agent safety benchmarks reproduce and evaluate various simulation scenarios to test systemic risks that appear when many AI agents interact, even when individual agents are safe.

You can test governance mechanisms for AI safety by running governance experiments with different configurations of audits, staking, and sanctions to observe their effects on multi-agent interactions.

Yes, you can use numpy and pandas to calculate systemic risk metrics for AI agents, as the simulation requires these dependencies to process interaction-level safety data and governance configurations.

To run a multi-agent simulation with three agents in Python, execute the command `python examples/illusion_delta_minimal.py` to calculate the illusion delta metric for systemic risk assessment.

You need simulation scenarios and governance configurations as inputs to assess multi-agent systemic risks, measuring emergent failures through safety benchmarks and interaction-level metrics.