When AI Agents Lie to Each Other
Picture this. You build a multi-agent system. Agent A does research. Agent B writes content. Agent C checks the work. Agent D publishes it. It looks clean and smart on paper. Each agent does one job well.
Then one day, Agent A gets a fact wrong. Not obviously wrong. Confidently wrong.
Agent B reads that wrong fact and builds on it. Agent C checks the combined output but cannot see Agent A's original source. Agent D sends everything out. Three layers of AI "checking" happened. Nobody caught the mistake. Why? Because each agent assumed the one before it had already checked the data.
This is called hallucination propagation. If you run multi-agent systems in production today, you are very likely already dealing with this problem. You just may not have a name for it yet.
The Numbers Are Worse Than You Think
Let me share some numbers before we...
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