Stationary Alone, Drifting Together: Adapting to Interaction-Mediated Nonstationarity in Multi-Agent Reinforcement Learning

External conditions can change how agents interact through shared infrastructure without changing their dynamics in isolation. This drift persists with frozen partners and vanishes for a lone agent. We formalise this interaction-mediated nonstationarity as a family of partially observable Markov games indexed by severity and introduce three public benchmarks for routing, voltage control and cooperative lifting, preserving their original rewards. We propose PACT (Peer-Action Coupling Tracking), a wrapper for unmodified multi-agent learners. Each agent predicts disturbances from peers' broadcast actions and the infrastructure layout using an online model whose size is independent of fleet size. A trust level controls compensation or steering, with zero trust recovering the host exactly. In a linearised feeder, predictive compensation with exact estimates raises the instability threshold without bound as trust approaches its maximum. In a routing model, excessive trust causes oscillations that make the route predicted to be faster become slower. Experiments against representative baselines demonstrate consistent improvements over the host learners across all benchmarks.