GRACE-Bench: Can Coding Agents Make Game Systems Work Together?

Coding agents are increasingly used to implement features and repair defects in existing software projects. Game projects make this work especially demanding: a single requirement often spans player input, physics, gameplay rules, object lifecycles, and visual feedback, so meeting it requires these systems to work together through shared runtime state and event sequences. To test whether coding agents can meet such complete game-development requirements, we introduce GRACE-Bench: 60 extension and repair tasks in existing Godot 2D projects, with 1,270 executable checks organized into gate, behavior, and integration layers. The layers cover execution prerequisites, designated mechanisms, and required cross-system interactions of the same task, so each answer is judged against its complete requirement and each failure is located in a layer. Across 12 models, 180 answers validly pass every gate and behavior check, so an evaluation without the integration layer would count all of them as complete; only 68 also pass every integration check. The other 112 (62.2%; task-resampling 95% interval 50.8–72.6%) fail checks of cross-system behaviors required by their task specifications. These failures span 33 tasks and all 12 models, and the proportion stays above one half when any single model or task is left out or when the ten most frequently failed integration checks are removed. These results expose a gap in current coding agents: they often implement the requested mechanisms without making them work together, a gap that checks of individual mechanisms alone would not reveal. Recorded runtime evidence and targeted replay trace these failures to unmet requirements and their gameplay consequences, showing the diagnostic value of the layered checks. Because every answer is graded on all checks of its task, GRACE-Bench can be rerun to test whether future agents genuinely improve at meeting complete development requirements.