Dynamic Governance of Multi-LLM Agent Systems for Collaborative Conversational Outcomes
How control theory and contextual bandits prevent agentic goal collapse in multi-LLM workflows
When two LLM agents with structurally opposed objectives interact across multiple turns, the absence of a shared goal function produces not competition but collapse: the visitor capitulates, the site agent stops varying its approach, and the conversation terminates without achiev...
ArXiv CS.AI
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This research demonstrates that complex multi-agent LLM systems require external control-theoretic governance to prevent conversational collapse and ensure goal-oriented outcomes. By implementing orchestration layers like Contextual Bandits, developers can move beyond passive prompting to actively steer AI-to-AI interactions toward measurable business objectives.
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