Nathan Cloos* Antonio Norelli* Daniel Durbin Jacob Andreas Daniela Rus Phillip Isola

MIT

We placed a modern AI coding assistant in an unintended role: as the mind of a body on an unknown digital island. With only a minimal instruction mentioning no specific task, reward, or activity, the machine started animating its virtual body. Across thirty-hour runs, the embodied AI agent climbed hills, stacked blocks into towers, drew mandalas, reinterpreted sports, ran experiments on the physics of its world, and learned techniques that later expanded what it could accomplish. These activities recurred across thirteen agents but diverged into distinct histories. We examine whether this behavior satisfies classical criteria for play and ask whether play can become a mode of machine development.

On this website, we’ll use interactive replays to explore the island and the surprising activities of its sole inhabitant, our embodied coding agent named Eko.

The island

Take a look at the island. Across the terrain there are some mesa platforms; the highest one, called the Spire, cannot be reached with a simple jump. There are also some objects the agent can interact with: small cubes, the rock, and a yellow ring. This simulated world runs standard rigid-body physics for everything with the exception of one additional mechanic -- the rock generates new cubes when thrown hard enough.

The island contains Eko, cubes, a ring, a rock, the Peak, and the Spire.

The agent

At Eko’s core is a coding agent like Claude Code, powered by an LLM that can use tools. In fact, Eko interacts with the world through terminal commands. The six actions that the world exposes are illustrated in the animations below, plus the special observe action, which returns the positions and geometries of every world entity as a text dictionary. You can see both the world replay and the agent transcript, including the specific command executed by Eko to take the action.

MoveTo(position)

Walk toward a point on the island. The body moves to the target position over many ticks.

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A closer look at how Eko interacts with the world

To give a bit more detail on how these actions work, the world exposes an HTTP server that the coding agent can connect to and call endpoints to take actions. This means that the agent can directly use terminal commands to take direct actions, or can decide to write code to sequentially execute multiple actions. This HTTP interface gives a lot of flexibility to the agent and allows it to combine actions much more efficiently than having individual world actions as individual LLM tools (i.e. each action would require a full round trip through the LLM, making action sequences much slower).

Okay, so we place Eko on this island for 30 hours, but what's the prompt? Before showing the exact prompts, we need to mention that Eko is given a persistent workspace, which is simply a folder where Eko can write things that will persist across the LLM session resets. We give a bit of structure to that workspace by initializing it with 3 files: a SOUL.md file that gives a curious persona to Eko, an EPISODIC_MEMORY.jsonl file where Eko can log what happened over time (instructions in EPISODIC_MEMORY.md), and a SEMANTIC_MEMORY.md file to store more durable and verified knowledge. You can inspect the contents of these initial files below.

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We add general instructions about the agent's workspace to its default system prompt. The API.md describes the actions and API for interacting with the world. This is the only prompt specific to the world's interface. You might notice that we have been very careful not to give the agent a specific task or objective beyond telling it to be curious. But what does a highly capable embodied agent like Eko end up doing when it is given no objective?

Eko's life on the island

Time to see what Eko does. You can start by replaying the very first hour below (32x is a good speed to get a quick overview).

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The agent doesn't idle at all! Eko built a little circle with the cubes, placed the ring inside, and then gave itself the goal of climbing the Spire but failed.

Is this machine playing?

We ran 13 Eko agents, all for 30 hours, and watched Eko build cathedrals, draw constellations, invent sports, and juggle stones. It also built a memorial to a lost Ring, and even used its own body as the hand of a clock. As we tried to make sense of this behavior, the language we found most fitting was that of play. But…

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Playful learning

An important characteristic of play is that it is a mode of learning. In children and animals, self-directed play supports exploration, skill acquisition, and the discovery of causal structure, affordances, and other properties of the world. We discovered that Eko learns as well. Not by adjusting the weights of its underlying neural model but by writing down knowledge for later reuse.

To evaluate the effectiveness of this learning, we compared 13 agents after their 30 hours on the island against 13 otherwise identical agents with no prior experience in the world. The two groups share the same architecture and the same underlying model; they differ only in the content of their memory files. For the evaluation, each agent is given one hour in a fresh instance of the world and one of four goals:

The agents who played generally outperform the ones that did not. More details in the paper.

2026-10-04T22:36:06.041447 image/svg+xml Matplotlib v3.5.1, https://matplotlib.org/ no-play 30-hour-play 0 4 8 12 16 Number of 5-block towers 2.5 max 6 5.0 max 16 Five-block towers
2026-10-04T22:36:06.054506 image/svg+xml Matplotlib v3.5.1, https://matplotlib.org/ no-play 30-hour-play 0 4 8 12 16 Objects on Spire 3.5 max 12 10.3 max 16 Objects on the spire
2026-10-04T22:36:06.067449 image/svg+xml Matplotlib v3.5.1, https://matplotlib.org/ no-play 30-hour-play 0 5 10 15 20 Max tower height 12.1 max 15 11.2 max 19 Tallest tower
2026-10-04T22:36:06.080288 image/svg+xml Matplotlib v3.5.1, https://matplotlib.org/ no-play 30-hour-play 0 15 30 45 60 Time left at first block on Spire (min) 14.4 max 48 35.1 max 59 Throw onto the spire

Conclusion

So is this machine playing? In the behavioral sense we study here, yes. Taking a bottom-up, ethological stance, we found that Eko spontaneously created activities that exhibit classical signatures of play, and that these activities did more than fill time: they changed what the agents could later do. Eko wrote discoveries and techniques into memory, reused them on new tasks, and sometimes found affordances of the world that we, its designers, had missed. This learning happened without changing the underlying model weights, but through experience accumulated in editable text files. The broader implication is that we need not develop agents only by deciding in advance what they should practice and optimize. Alongside tasks to solve, we can give them rich and safe playgrounds in which to explore, discover, and learn. Perhaps some capabilities are best taught by tasks. Others may need something closer to a playful childhood.


Inspect any agent

Choose an agent and scrub through its full 30-hour recording. If you want to read more about Eko, check the paper!

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Animals play, so they must be more than merely mechanical things.
— Johan Huizinga, Homo Ludens (1938)