This paper discusses the dynamic governance of multi-LLM agent systems. Research shows that when two LLM agents have structurally opposed objectives, the absence of a shared goal function leads to the collapse of the conversation. To address this issue, the authors propose the Experience Orchestrator (EO), a control-theoretic governance layer that can substitute for the missing goal function. EO governs the conversation through three mechanisms: content selection, behavioral consistency control, and intent tracking. In a simulated financial services environment, EO achieves a significant lift in high-intent advisor contact rate (+32 percentage points). The results show that the governance policy is the key factor determining the outcome of the conversation, rather than the initial conditions of the environment. Blogger's Review: This paper proposes an innovative governance layer to solve the collaboration problem in multi-LLM agent systems, with simulation experiments verifying its effectiveness. However, future work needs to validate the governance layer's effectiveness and robustness in real human-computer interaction environments.