I Built a Full Game in One Day Using AI Agents — Here's What Happened

The Challenge: A Complete Game Prototype in 24 Hours

What if you could build a full game prototype — not a toy demo, but something with 14 interconnected systems — in a single day?

That's exactly what I did with Somnia, a cozy life-sim RPG built in Godot 4.6 with GDScript. The twist: I orchestrated a team of AI sub-agents working in parallel, each with a dedicated role. Here's the honest breakdown of what worked, what surprised me, and what I'd do differently.

The Setup: An AI Agent Team, Not "AI Writing Code"

Let me be clear — this wasn't "paste a prompt into ChatGPT and get a game." I structured a multi-agent pipeline where each agent had a specific role:

These agents worked in parallel — while the Architect was designing the combat system, QA was already writing tests for the farming system that Lead Dev had just finished. The PM kept everything sequenced so agents weren't blocked on each other.

This is the key insight: the value wasn't in any single agent's output. It was in the orchestration.

TDD as the Backbone

I enforced a strict rule: tests first, implementation second, all tests green before moving on.

This wasn't optional. Every system followed the same cycle:

1. QA writes test cases based on the Architect's spec
2. Lead Dev implements until tests pass
3. Security reviews for edge cases, QA adds regression tests
4. Move to the next system

By the end of the day: 840+ tests, all passing.

Why does TDD matter so much with AI agents? Because AI-generated code has a specific failure mode — it looks correct but handles edge cases poorly. Tests catch that immediately. Without TDD, I'd have spent the second half of the day debugging subtle integration bugs instead of building new systems.

The 14 Systems

Here's what Somnia shipped with after one day:

1. Farming — Planting, watering, growth stages, seasonal crops
2. Combat — Turn-based with elemental affinities and status effects
3. Fishing — Minigame with rarity tiers and location-based catches
4. Dream Weaving — The signature mechanic: craft dreams that affect the world
5. Dungeon Generation — Procedural rooms with scaling difficulty
6. Weather System — Dynamic weather affecting crops, fishing, and NPC behavior
7. NPC System — Schedules, relationships, gift preferences
8. Quest Engine — Multi-step quests with branching outcomes
9. Home Decoration — Furniture placement with grid snapping
10. Inventory — Stack management, categories, quick-slots
11. Save/Load — Versioned save files with migration support
12. Audio Manager — Adaptive music and spatial sound effects
13. Day/Night Cycle — Lighting changes, time-gated events
14. UI Framework — Menus, HUD, dialogue boxes, notifications

Each system is modular. The Weather system doesn't know about Farming directly — it emits signals that Farming listens to. This decoupled design was the Architect agent's biggest contribution.

What Actually Surprised Me

1. The PM agent was the most valuable

I expected Lead Dev to be the star. Wrong. The PM's task sequencing eliminated almost all blocking dependencies. When you have 6 agents working in parallel, coordination is the bottleneck, not coding speed.

2. Security agent caught real issues

I almost skipped the Security agent — "it's a single-player game, who cares?" But it caught save file tampering vulnerabilities, integer overflow in the inventory stack system, and a dream weaving exploit that let you duplicate items. These would have been painful bugs later.

3. 840 tests sounds like a lot. It's not enough.

Integration tests between systems were thin. Unit tests were solid, but "what happens when it rains during a dungeon run while the player is fishing" — those cross-system scenarios need more coverage. Lesson: with AI agents, you can afford to write way more tests than you think.

4. GDScript + Godot 4.6 was the right call

GDScript is simple enough that AI agents generate it reliably. C++ or Rust would have introduced compilation errors and memory bugs that would have killed the one-day timeline. Match your language to your constraint.

The Workflow in Practice

A typical 30-minute cycle looked like this:

[00:00] PM assigns: "Implement fishing minigame"
[00:02] Architect delivers: component diagram + signal contracts
[00:05] QA writes: 47 test cases for fishing mechanics
[00:08] Lead Dev starts implementation
[00:20] Lead Dev: all 47 tests passing
[00:22] Security review: adds input bounds on reel tension
[00:25] QA: 3 additional edge case tests
[00:28] Lead Dev: all 50 tests green
[00:30] PM: "Moving to Dream Weaving system"

Six of these cycles ran in parallel across different systems. That's how you build 14 systems in a day.

What I'd Do Differently

More integration tests from the start. I'd have the QA agent write cross-system tests as soon as the second system is complete.

A dedicated Refactor agent. After 10+ systems, some early code needed cleanup. I did this manually; an agent could have handled it.

Stricter interface contracts. The Architect defined interfaces, but some agents drifted. Automated contract checks would catch drift immediately.

Try It Yourself

The prototype is playable: Somnia on itch.io

It's rough — one day of work is one day of work. But it's a real prototype with interconnected systems, not a hello-world with a sprite.

The Takeaway

The question isn't "Can AI write code?" — it obviously can. The real question is: "Can you design a system where multiple AI agents collaborate effectively?"

The answer is yes, but only if you:

• Define clear roles and interfaces
• Enforce TDD ruthlessly
• Invest in orchestration (the PM agent)
• Pick a tech stack that minimizes friction

14 systems. 840+ tests. One day. The game dev landscape is shifting, and the developers who learn to orchestrate AI teams will build things that were previously impossible solo.

Have questions about the multi-agent setup or want to try this approach on your own project? Drop a comment — happy to share specifics.

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