Current AI systems (world models, large language models, and joint-embedding predictive architectures) learn representations in epistemic isolation. Each system interacts with its environment but never with other agents in ways that constrain its representational structure. This paper argues that epistemic isolation has a specific, measurable consequence: representational instability under distributional shift. We draw on two converging philosophical arguments. Quine’s inscrutability of reference and structural realism establish that representational stability cannot come from internal architecture alone; proxy functions guarantee that any arrangement can be systematically reinterpreted. What constrains admissible interpretations is social coordination. Davidson’s triangulation argument establishes that the concept of error, meaning the distinction between model failure and environmental change, requires a second perspective. We translate these insights into five architectural constraints for multi-agent systems: Multiple Perspectives, Observable Reactions, Conflict Detection, Normative Pressure, and Structural Invariance. Each constraint is traceable to a specific philosophical argument. We then propose the Gavagai Shift experiment, a controlled paradigm for testing whether social coordination constraints improve representational stability under distributional shift. The experiment uses a grid-world in which object appearances change while functional roles are preserved, comparing isolated agents with socially constrained agents across three metrics. The contribution bridges naturalized epistemology and AI architecture: philosophical theory motivates specific engineering predictions, and experimental design tests them.