Real lessons from using AI agents in game production
Luden.io’s production experience shows that AI agents are more useful for narrow, checkable tasks than for building whole game features on their own. They helped with finding slow code, narrowing down bug causes, suggesting unusual test cases, reviewing design documents, building small , updating static websites, and analyzing social or in-game data. Results improved when the agent could read text-based material such as save files, logs, game state, replay data, build output, and design documents.
The team also used with to update design docs after meetings and create for review. The weaker areas were full gameplay feature , fully autonomous playtesting, multi-agent peer review, final production art, complex game UI, and scene editing. Complex feature work stayed fragile because many small judgment calls are obvious to a human game developer but easy for an agent to miss.
The team suggests an “LLM Week”: spend one week using AI on real daily tasks, then sort tasks into what works, what works but is too annoying, and what still fails with clear reasons. For beginners, the practical path is a short AI agent workshop first, then a second step focused on automating routine work or frequent checks.
Key points
- AI agents helped most with bug analysis, review, test ideas, document review, small automation, and analytics.
- Text-based inputs such as logs, save files, game state, and design docs made agent work much more useful.
- Whole gameplay features and fully autonomous playtesting were still too fragile for reliable production use.
- Clear and smaller documents helped agents understand where changes belong.
- An “LLM Week” can turn vague AI excitement into a practical list of tasks that work, tasks that are painful, and tasks that fail.