Experience Orchestrator posts 78.1% advisor contact rate versus 46.1% naive LLM baseline across 60,000 simulations

The paper models two LLM agents with opposed objectives in a simulated financial services conversation. Without a shared goal function the visitor agent collapses into compliance or the site agent stops exploring, terminating trajectories before either objective is met. The Experience Orchestrator inserts three control mechanisms: a contextual bandit selecting content arms from web analytics, a PID controller enforcing schema constraints for behavioral consistency, and a POMDP belief tracker updating visitor intent probabilities. Contextual bandit arm selection explained 97% of between-factor variance in final outcomes. Persona-level breakdowns show the governance layer supplies the decisive difference for visitors with low natural conversion inclination, while visitors already near alignment perform adequately under unmodified LLM defaults. All results remain conditional on LLM-to-LLM simulation; the PID component lacks calibration against live human response distributions. Operational deployment therefore hinges on live-traffic validation. The reported lift is an upper bound measured inside a closed simulation loop. Real visitor stochasticity will test whether the POMDP belief updates and PID schema constraints retain their simulated effectiveness once external noise enters the loop.