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Multi-agent AI systems can run into trouble even when their individual agents are behaving correctly, an Agentic AI Foundation blog post says. It points to uncoordinated goals, missing stopping conditions and conflicts over shared infrastructure as potential causes.
Why it mattersThe post describes proposed fixes including a central deterministic referee, peer-to-peer negotiation and explicit commitments. In other words, getting agents to work together may require more than making each one better at its own job. Coordination is not a glamorous feature, but neither is an outage.
Discuss: Should multi-agent systems rely on a central referee, or is peer-to-peer coordination a better way to prevent failures?
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