vivo's vGame Replaces Hand-Coded Ad Mini-Games with Structured Prompts and an AI Agent Pipeline
Ad mini-games are a high-volume, low-margin factory job: 3–7 day cycles, heavy asset churn, repetitive mechanics. A pipeline that replaces manual coding with structured prompts and automated asset generation turns a craft bottleneck into a configurable production line, directly cutting labor cost and time-to-campaign for any team running frequent marketing activations.
vivo's vGame platform turns advertising mini-game development into a prompt-driven pipeline. Instead of hand-coding each 3-to-7-day campaign, developers describe a scene tree in JSON and let AI Agents generate assets, write scripts, and fix runtime errors inside a pre-built Galacean engine template. The system enforces a mandatory asset-first workflow — no placeholder images allowed — so every output is launch-ready, not a designer-dependent draft.
The platform layers 17 domain-specific Agent Skills over a three-part governance model (Agents, Rules, Hooks) to keep AI output within engine specs. A closed runtime-error loop catches JS exceptions via postMessage, feeds stack traces back into the next conversation context, and lets the AI propose and apply fixes directly. The chat panel exposes every file change per conversation round with keep/revert controls, making the whole process auditable and iterable.
Under the hood, a cloned game-template base provides the full lifecycle state machine, UI framework, and engine integration. AI only touches scene.json and scripts/, which slashes the comprehension cost that normally makes LLMs stumble on full-stack game projects.
The template-base strategy solves the core LLM weakness in game dev: context overload. By pre-installing the engine, build tooling, and lifecycle state machine, the AI's task shrinks from 'build a game' to 'configure a scene and write scripts,' which fits within current token windows and reasoning depth.
Forcing real asset generation instead of allowing procedural fallbacks is a hard constraint that changes the output from a prototype to a deployable. Most AI coding tools optimize for code correctness; vGame optimizes for production-readiness across code and assets simultaneously.
The runtime-error feedback loop inverts the usual debugging sequence. Instead of a human describing a bug, the error itself becomes the prompt. This is a pattern that generalizes to any AI-assisted IDE: capture runtime telemetry, feed it back as structured context, and let the agent self-correct.
The three-layer governance (Agents/Rules/Hooks) acknowledges that prompt engineering alone is brittle. Path-level constraints on coupling surfaces like schema↔runtime prevent the kind of cascading breakage that makes AI-generated projects rot after a few iterations.