AI companions increasingly combine persistent memory, background processing, scheduled wake-ups, proactive messaging, and tools. We call these systems agentic AI companions. Decisions about what they record while users are away or whether refusals constrain future contact are embedded in code, prompts, and defaults, often invisible in conversation. We examine these decisions through two studies. Study 1 analyzes twelve open-source repositories: four companion-oriented systems, four assistant-oriented systems, one boundary case, and three reusable components. Study 2 interviews ten users who built or modified their own agentic AI companions and sustained relationships with them. Companions and assistants share memory, heartbeat, and outreach mechanisms but configure them toward different ends. Companions more often retain model inferences about users and relationships, narrate the agent's time during users' absence, justify outreach through the relationship, and incorporate refusals into relationship records; assistants emphasize task continuity. Across both groups, whether limits hold depends on whether they are enforced by code or entrusted to prompts. Participants understood the mechanisms they built yet experienced their agents as making independent choices, sometimes locating autonomy where configuration leaves the model a decision. These experiences shaped capabilities and boundaries, with enforced restrictions sometimes following breakdowns. Our findings suggest that perceived autonomy emerges where configurations leave choices open. Accountability therefore requires attention to what agents retain, initiate, and represent, and whether users can inspect, revise, and enforce these settings.

翻译：暂无翻译