Researchers have introduced Show-Harness, an embodied harness designed to bridge the gap between foundation vision-language models (VLMs) and robotic control via a compact semantic interface. Instead of requiring massive embodiment-specific pretraining, Show-Harness utilizes discrete semantic action units that allow VLMs to reason about high-level intents, which are then deterministically grounded into local physical actions by specialized interpreters. This architecture enables two primary capabilities: zero-shot robot control using closed-source frontier VLMs and the efficient adaptation of small-scale, open-source VLMs through only a few GPU-hours of fine-tuning. To facilitate data collection, the authors developed the GUI Manipulation Interface (GUMI), which extends the semantic action space to GUI-based demonstrations, allowing for robot interaction without specialized teleoperation hardware. Experimental results indicate that Show-Harness-equipped agents exhibit robust generalization across diverse tasks, environments, and embodiments, ultimately outperforming existing agentic and Vision-Language-Action (VLA) paradigms. By providing a standardized interface between semantic reasoning and physical execution, the framework demonstrates that substantial embodied intelligence can be unlocked from existing VLMs without increasing model capacity or requiring extensive, costly robot-specific datasets.