Engineering an AI Agent that Ships Real Roblox Games

说明与用途

一种工程化方法，利用AI代理自动构建、测试并发布完整的Roblox游戏，采用已测试组件库、严格类型检查和自动错误修复循环。

问题

How to produce shippable Roblox games from an LLM‑driven agent while ensuring reliability, testability, and deployability.

结果

The agent shipped three monetized Roblox games that survived real‑device testing, demonstrating that a library‑based, type‑checked, automated playtest loop can reliably produce shippable content.

工作流

• 1. Agent loads a library of 136 tested kits.
• 2. Agent composes a game inside Roblox Studio via a local WebSocket.
• 3. CI runs luau‑lsp strict type checks on all kits.
• 4. Agent snapshots the place, runs a 30‑second playtest, collects errors, and sends fixes back to the LLM.
• 5. After three failed attempts, the agent restores the snapshot.
• 6. Successful build is built with Rojo and deployed via a curl POST to the Roblox API.
• 7. Post‑deployment tasks (icons, thumbnails) are handled manually or via dashboard.

证据

The agent has now shipped three Roblox games: a pet daycare, an outback tycoon, and a hidden‑object island hunt.

来源

Every kit passes luau‑lsp analyze in strict mode on CI, with a Rojo‑generated sourcemap so cross‑kit requires resolve.

来源

The newest piece is a self‑repair loop. After the build phase, the companion snapshots the place, runs the game with RunService:Run() for 30 seconds, collects every error since the run started, classifies them, and hands them back to the LLM to fix with a script update.

来源