In evacuation modeling, architectural configurations are typically treated as fixed constraints, limiting the exploration of adaptive design strategies that respond dynamically to occupants’ movement patterns. This research investigates environment-mediated coordination in a prototypical multi-agent reinforcement learning (MARL) setting. Using Proximal Policy Optimization (PPO), a navigating agent learns escape trajectories, while a design agent modifies door placement solely based on observed movement, without explicit communication. The system was evaluated in a multi-room environment to explore how complementary strategies evolve when navigation performance and immediate floor plan changes are coupled. The results demonstrate successful implicit coordination, with substantial reductions in episode length as the door controller agent progressively shifted door placements towards positions that facilitated faster escape paths. Navigation trajectories converged on paths that maximized the reward of the navigating agent, while door placement strategies minimized evacuation times. Therefore, although allowing architectural changes during episode runtime, the results still show emerging adaptive behaviors of both agents and demonstrate the feasibility of coordinating navigation and design actions through dynamic environment modification. This approach paves the way for adaptive architectural design systems that can dynamically respond to user behavior patterns, with potential applications in performance-based building design tools and in emergency planning optimization.