Agents built on vision language models (VLMs) now compete with learned policies on robot manipulation leaderboards. We ask how far a coding agent can go when it controls a robot instead of a computer, and where it stops. Our system, HexaShell, gives the agent a thin harness: one command line that turns its commands into robot motion and makes no decisions of its own. On the robot, the agent keeps doing what it does best: it reads output, writes code and runs tests. Two agents of the same model share the work. An acting agent runs each episode. After a failure, an optimizer agent reads the full record of the episode, with every command, image and message, finds the cause, and writes or fixes small programs called tools, each with its own tests. What the agents learn stays as code, and no demonstrations or training are needed. At run time, the tools use only what a real robot also has: RGB images, camera calibration and the state of the arms. They find objects by triangulation between camera views. Because these inputs are the same in simulation and on hardware, the tools move to a real robot with a different arm after only a small change for the arm. We build the tools on ten layouts of one seed per task with Codex and GPT-6-Astra and freeze them; the eight Open tasks share one set of tools. In RoboDojo's official evaluation on a hidden seed, 50 episodes for each of the 42 tasks, HexaShell reaches 46.9% success and a progress score of 53.2, the state of the art on the official RoboDojo leaderboard. The limit is contact, not reasoning: memory tasks reach 81.0%, but precision tasks only 32.0%. We open source the tools evolved so far at https://github.com/HexaFuture/HexaShell.

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