Bridging the Multi-AI Agent Gap: Emphasizing Simplicity in AI Collaboration and Implementation Insights
Researchers from UC Berkeley found that over 75% of tasks performed by multi-agent systems failed in their study of 151 task runs across five frameworks. The main issues were unclear system specifications, lack of coordination among agents, and weak task verification processes. To improve outcomes, experts suggest defining clear roles, using verification AI agents, standardizing ...
