An AI engineer deployed an agent benchmarking game in under an hour

Pratyush wanted to drop two large language models into a puzzle maze and watch them navigate it. The research value sits in how different models reason under constraint: which paths they pick, where they stall, how they adapt. The concept was clear. Translating it into a deployed app meant solving an infrastructure problem that had nothing to do with the experiment itself.

Deployment is often where momentum dies. For builders who think in models and agents, the gap between a working concept and something live and shareable is a context switch that costs real time. Pratyush was working through that gap manually when the conversation started.