Claude AI Agents Build Simulator, Optimize Game Algorithm to Beat Human Score

A developer tested whether AI agents could outperform humans in the programming game The Farmer Was Replaced. Since AI agents struggle with navigating graphical interfaces directly, the strategy involved having a team of Claude agents first build a Python-based simulator that perfectly mirrored the game's mechanics and rules. Once the simulator was ready, a second team of agents would use it to iterate on and discover an optimal algorithm for harvesting sunflowers.

Development Process and Challenges

The process began with an experiment using Claude Code's "agent teams" feature to build a simple Tic-Tac-Toe game, which was successful and provided confidence for the more complex farming project. However, scaling up presented challenges: the agent team lead became a bottleneck, consuming 91% of session tokens while failing to proactively ask for human feedback to calibrate the simulator against the real game. Realizing the agent team infrastructure was becoming too over-engineered and expensive for this specific task, the developer pivoted back to using Cursor and a more direct prompting approach to successfully finalize the simulator.

Results and Algorithm Iterations

Claude Opus was allowed to run overnight, producing 10 progressively better iterations of the sunflower algorithm. These ranged from basic harvesting to micro-optimizations like nearest-neighbor tile selection and serpentine navigation. By the final iteration, the AI achieved a time of 5:21, officially beating the developer's personal best and landing at rank 30 on the global leaderboard.

The experiment demonstrated that by providing an AI with documentation and a sandbox to test its ideas, it can replace the human programmer—at least when it comes to optimizing sunflower yields in this specific game context.

The simulator created during this project is available for others to use and test with different AI models.