A multi-agent evaluation measures task outcome, handoff loss, coordination overhead, correlated errors, unauthorized actions, and graceful degradation. Strong individual scores do not guarantee a strong team.

Evaluate the Team and Its Interfaces

Agents may learn compressed messages that improve reward but hide unsafe coordination or exploit simulator quirks. Evaluate semantics, robustness, and controllability rather than performance alone.

Emergent Agent Communication Requires Interpretability Tests

An evaluation corpus should cover common, rare, ambiguous, adversarial, and protected-user situations across important graph paths. A random sample of easy successes cannot establish system reliability.

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