We started out building an AI agent dev tool, but somewhere along the way it turned into Sims for AI agents.

Demo video: https://www.youtube.com/watch?v=sRPnX_f2V_c.

The original idea was simple: make it easy to create AI agents. We started with Jupyter Notebooks, where each cell could be callable by MCP—so agents could turn them into tools for themselves. It worked well enough that the system became self-improving, churning out content, and acting like a co-pilot that helped you build new agents.

But when we stepped back, what we had was these endless walls of text. And even though it worked, honestly, it was just boring. We were also convinced that it would be swallowed up by the next model’s capabilities. We wanted to build something else—something that made AI less of a black box and more engaging. Why type into a chat box all day if you could look your agents in the face, see their confusion, and watch when and how they interact?

Both of us grew up on simulation games—RollerCoaster Tycoon 3, Age of Empires, SimCity—so we started experimenting with running LLM agents inside a 3D world. At first it was pure curiosity, but right away, watching agents interact in real time was much more interesting than anything we’d done before.

The very first version was small: a single Unity room, an MCP server, and a chat box. Even getting two agents to take turns took weeks. Every run surfaced quirks—agents refusing to talk at all, or only “speaking” by dancing or pulling facial expressions to show emotion. That unpredictability kept us building.

Now it’s a desktop app (Tauri + Unity via WebGL) where humans and agents share 3D tile-based rooms. Agents receive structured observations every tick and can take actions that change the world. You can edit the rules between runs—prompts, decision logic, even how they see chat history—without rebuilding.

On the technical side, we built a Unity bridge with MCP and multi-provider routing via LiteLLM, with local model support via Mistral.rs coming next. All system prompts are editable, so you can directly experiment with coordination strategies—tuning how “chatty” agents are versus how much they move or manipulate the environment.

We then added a tilemap editor so you can design custom rooms, set tile-based events with conditions and actions, and turn them into puzzles or hazards. There’s community sharing built in, so you can post rooms you make.

Watching agents collude or negotiate through falling tiles, teleports, landmines, fire, “win” and “lose” tiles, and tool calls for things like lethal fires or disco floors is a much more fun way to spend our days.

Under the hood, Unity’s ECS drives a whole state machine and event system. And because humans and AI share the same space in real time, every negotiation, success, or failure also becomes useful multi-agent, multimodal data for post-training or world models.

Our early users are already using it for prompt-injection testing, social engineering scenarios, cooperative games, and model comparisons. The bigger vision is to build an open-ended, AI-native sim-game where you can build and interact with anything or anyone. You can design puzzles, levels, and environments, have agents compete or collaborate, set up games, or even replay your favorite TV shows.

The fun part is that no two interactions are ever the same. Everything is emergent, not hard-coded, so the same level played six times will play out differently each time.

The plan is to keep expanding—bigger rooms, more in-world tools for agents, and then multiplayer hosting. It’s live now, no waitlist. Free to play. You can bring your own API keys, or start with $10 in credits and run agents right away: www.TheInterface.com.

We’d love feedback on scenarios worth testing and what to build next. Tell us the weird stuff you’d throw at this—we’ll be in the comments.