LLM-based embodied agents are increasingly evaluated on environment-grounded, long-horizon planning, yet existing benchmarks rarely test whether an agent can condition its plans on the specific user it serves. This gap points to a broader limitation in embodied task-planning systems, which often ground their reasoning only in the physical environment while overlooking the diversity of the people they serve. We frame this problem as Planning with Human Attributes (PHA), in which an agent must integrate explicit user information into long-horizon embodied task planning. We introduce Trinity, a benchmark designed to evaluate PHA capabilities. Within Trinity, a single instruction can yield different, equally valid, or even opposing outcomes depending on a user’s personal attributes. Our experiments reveal that standard off-the-shelf in-context learning-based agents commonly fail at the human-grounding component, whether or not they are provided with relevant examples, highlighting a key obstacle for PHA tasks. Beyond benchmarking, we contribute a bottom-up failure-mode analysis that isolates where and why agents break, and we distill it into concrete, model-agnostic design principles for human-agent-environment (HAE) aligned agents. We hope Trinity brings domestic robots a step closer to becoming trusted, socially acceptable partners.