You can improve multi-agent system reliability at inference time by filtering and correcting agent outputs, without expensive retraining.
AgentDropoutV2 fixes errors in multi-agent AI systems without retraining. It works like a quality filter at test time—catching bad outputs from individual agents, correcting fixable errors using past failure patterns, and removing unfixable ones to prevent mistakes from spreading. The system improved math problem accuracy by 6.3% on average.