Where the Harness Meets the Physical World: A Survey of the Physical Agentic Harness

Abstract

Embodied foundation models now allow robots to act on open-ended instructions, yet success on long-horizon physical tasks also depends on the runtime around the model, which organizes the task, invokes skills, guards execution, and retains experience. Digital agent research treats this runtime, known as the harness, as a first-class design object that can be engineered and even improved from experience, since the same model can perform very differently under different harnesses. Such harness designs do not carry over directly, because physical execution is embodiment-specific, partially observed, time-constrained, and often irreversible, which also makes experience costly to learn from. Embodied AI systems increasingly build similar runtimes, yet the field still lacks a unified view of them. This survey treats the physical agentic harness as a research object in its own right. We define it as the runtime system that maintains task and execution state for an embodied foundation model and mediates the model’s interaction with the physical world through one or more physical bodies. We then review over 200 works and organize them by six functional responsibilities: task orchestration, digital resources, embodied capabilities, execution governance, digital-physical interaction, and persistent evolution. This organization offers a common lens for comparing methods across communities, making explicit how model decisions reach physical execution and how grounded evidence flows back. The reviewed benchmarks and applications provide limited evidence of individual runtime contributions, particularly the effects of execution governance and sustained gains from persistent evolution. We close with open problems in model-harness co-design, capability composition, real-time decision making, distributed execution, and continual improvement from physical experience.