Picture two robots shoving each other over a soccer ball, and none of it is scripted. That is the scene at the center of a project a Reddit creator just dropped in r/PromptEngineering, called the Agentic World Cup. By sentence three, credit where it is due: this Redditor built a live 1v1 soccer arena where AI agents fight for the ball inside a real physics simulation, not a pre-rendered clip.
What’s new: the site lets you write a tactical prompt, hand it to an embodied agent, and watch that agent play soccer against another agent’s strategy. You are not tweaking a chatbot’s tone or asking it to write you a poem. You are coaching a body that has joints, momentum, and a ball that does not care about your feelings. The creator was explicit about this in the post: those are not animations. The agents are contending with actual simulated physics, in articulated bodies, in an environment where the opponent is not cooperating with you.
Here’s the twist. The outcome depends on two separate things stacked on top of each other: how well you prompt the tactics, and which base model is driving the agent’s brain. Claude, Gemini, and ChatGPT all read the same instructions differently, so a prompt that turns one model into an aggressive striker might turn another model into a hesitant defender that never commits to the tackle. The creator built the whole project around this idea, so prompt quality becomes something you can actually measure on a scoreboard, not just something you feel good about after writing it.
Here is the prompt that shipped with the project, reproduced exactly as the creator wrote it:
“I want you to play like an aggressive striker – don’t be afraid to push and shove if you need to. When you’re near the opponents goal, dont waste anytime and kick the ball in! Make sure the opponent agent doesn’t flank you to try and steal the ball!”
Notice what is doing the work in that one paragraph. It assigns a role (“aggressive striker”), gives explicit permission to break a soft social rule (“don’t be afraid to push and shove”), sets a hard trigger condition (“when you’re near the opponents goal”), and adds a defensive constraint on top (“make sure the opponent doesn’t flank you”). That is four distinct instructions bundled into a few sentences, and the agent has to translate every one of them into physical movement, in real time, against an opponent trying to do the same thing.
If you want to try coaching your own agent, here’s the mini workflow:
- ⚽ Pick your model first. Different LLMs bring different “personalities” to identical tactics, so test more than one before you judge the prompt itself.
- 📝 Write a role-based prompt. Assign a position (striker, defender, goalkeeper) before you assign behavior, the same way a coach talks to a player.
- 🎯 Add a trigger condition. Tell the agent exactly when to act, like “near the goal” or “the moment the ball is loose,” instead of a vague “play well.”
- 🛡️ Stack a defensive instruction on top. Offense without defense gets punished fast in a live match where the other side is also trying to win.
Watch the match, then rewrite. The simulation shows you exactly where the translation from words to action broke down, which is the whole point.
Pro tip: keep one clear objective per sentence. The example prompt above works because each instruction maps to a single physical decision, push, shoot, watch for flanks. Cram two ideas into one clause and the agent tends to average them into something mushy, which in soccer terms means your striker just stands near the box doing nothing useful.
Second pro tip: pay more attention to your losses than your wins. A win can happen because the opponent picked a weaker base model, not because your prompt was actually good. A clean loss, where your agent does exactly what you told it and still gets outplayed, tells you precisely which instruction in your prompt needs a rewrite. That is the fastest feedback loop you will get from this project.
Worth knowing before you dive in: this only works because someone had to build an actual physics engine and agent harness underneath the prompt box, which is a heavier lift than most prompt playgrounds you have tried. The creator is still gathering feedback on how well the agents translate intent into action, so expect some rough edges and the occasional agent that completely ignores your defensive instruction under pressure.
Most prompt playground demos let you admire the output and move on. This one keeps score. Go run a match, then come back and tell me which base model surprised you the most. ⚡
Built The Agentic World Cup: Prompting agents on how to win at 1v1 soccer.
by u/agenticworldcup in PromptEngineering