Foundation Models for Game Development and Maintenance

How is AI used in game development?

AI game development applies foundation models to the engineering work required to turn ideas and specifications into running, revisable games. Systems can generate or edit code, create scenes, configure assets, invoke engine tools, inspect runtime output, diagnose failures, repair bugs, and coordinate changes across a project.

Tool-using development agents go beyond code completion by acting inside engines and editors, where scripts, prefabs, scene graphs, resources, input mappings, physics, and build settings must agree. Execution feedback is essential: plausible code may compile yet fail at launch, violate a requirement, break another mechanic, or become difficult to maintain. Strong systems therefore work in a loop—propose a change, run or observe the game, interpret errors and behavior, then revise the project.

Long-term usefulness also depends on maintenance. Models should cope with changed requirements, regression tests, multiple project versions, documentation, and handoff to another developer. Evaluation should measure launchability, functional requirements, repair success, regression safety, iteration cost, and whether the result can be understood and modified later. This differs from design: proposing a level or mechanic defines an artifact, while development must implement it within executable engine constraints.

This topic is surveyed in AI for Games in the Foundation Model Era.