{"ok":true,"article":{"slug":"dynamic-governance-of-multi-llm-agent-systems-for-collaborative-conversational-o-def6cd07","title":"Dynamic Governance of Multi-LLM Agent Systems for Collaborative Conversational Outcomes","url":"https://arxiv.org/abs/2608.11207","canonical":"https://www.aimode.news/article/dynamic-governance-of-multi-llm-agent-systems-for-collaborative-conversational-o-def6cd07","sourceName":"arXiv cs.AI","summary":"arXiv:2608.11207v1 Announce Type: new Abstract: 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 achieving either agent's stated objective. This paper asks whether a control-theoretic governance layer can substitute for that missing goal function. The Experience Orchestrator (EO) addresses this in a simulated financial services environment where a site agent guides a visitor toward advisor contact while the visitor maintains psychologically realistic resistance. EO governs the joint trajectory through three mechanisms: a Contextual Bandit (CB) that selects content arms calibrated from real-world web analytics, a PID controller that enforces behavioral consistency via dynamic schema constraints, and a POMDP belief tracker that maintains a probabilistic model of visitor intent. Across 60,000 simulations, EO achieves a +32 percentage point lift in high-intent advisor contact rate (78.1% vs. 46.1% over a naive LLM control), with CB variant selection accounting for 97% of between-factor outcome variance -- confirming that the governance policy, not environmental initial conditions, determines where trajectories end up. Persona-level analysis reveals two distinct regimes: for visitors with no natural inclination toward conversion, the governance layer is the difference between a functional system and a non-functional one; for visitors already near alignment, a naive LLM's empathetic defaults are largely sufficient. All findings are conditional on LLM-to-LLM simulation. The PID controller has not been calibrated against real human unpredictability, and validating EO on live traffic is the critical next step.","category":"AI","image":null,"lang":"en","publishedAt":"2026-08-13T04:00:00+00:00","createdAt":"2026-08-13T04:00:14.554378+00:00"}}