Game-Evolver: Dual-Loop Evolution Elicits Better End-to-End Game Orchestration

Recent coding agents have made significant strides in tackling complex tasks across various domains. However, even frontier agents can solve only a minority of game generation tasks, owing to the extreme long-horizon requirements from building games and interacting in it. Moreover, most agent frameworks rarely provide comprehensive, holistic workflows specifically designed for game making tasks, preventing their further applications. These factors jointly pose a unique challenge in the context of agent game development. We address this challenge with Game-Evolver, a dual-loop framework that couples two agents. A game orchestration agent (GOA) builds the game; a harness proposal agent (HPA) proposes bounded changes to the GOA's harness. In the inner loop, the GOA A/B-tests each proposed harness against the best candidate so far. A harness is admitted only when the new harness passes dual-rubric verification, where hard rubrics enforce the game legality and soft rubrics measure its quality. In the outer loop, the HPA distills the GOA's completed runs into recurring failure modes, and uses them to update its own harness library. Game-Evolver thus improves both the game-making and the agent that generates it. Extensive experiments on four game benchmarks and six backbone models show that Game-Evolver improves every baseline, delivering several-fold gains on weaker models and up to 30% on the strongest. Our approach also transfers to general agent tasks, while showing impressive applicability to real game projects.