What I Built

Who said learning AI System Design has to be all dark cyberpunk terminal screens and exploding servers?

I built and open-sourced Prompt Paws: The Cozy AI Agent Cafe & Bakery (architected with Google Gemma 4 & Antigravity) - an interactive 60 FPS AI Agent Architecture Training & Simulation Platform disguised as a cozy pastel pastry bakery run by Robot-Cat Chefs! 🐱‍🍳🍰

Who would use it?

• Engineering Teams, Bootcamps & Developer Relations Programs training developers on production LLM architecture (where a single uncached system prompt or missing input guardrail can cost thousands of dollars in wasted tokens or latency spikes).
• Instead of static slide decks, Prompt Paws gives teams an interactive, visual sandbox where every pastry station maps 1-to-1 to a real production AI pattern:

🎮 Live Playable Demo & How to Play in 30 Seconds!

• 🕹️ Play Live in Your Browser (60 FPS HTML5 Canvas): 👉 https://robinamirbahar.github.io/cozy-ai-agent-cafe-game/
• 🔒 100% Client-Side Simulation ($0.00 API Cost & Zero Keys Required): The entire game runs locally inside your browser using a deterministic HTML5 Canvas simulation engine - no API keys, no sign-ups, and zero paid LLM tokens are ever used when you or your team play!

🐾 What Is the Core Gameplay Loop?

Think of Prompt Paws as a cozy Tower-Defense & Kitchen-Management game for AI Agent Architecture:

• Customer Pastry Orders (🥐, 🍪, 🌶️, 🍓, 🧋, 🍮) = Incoming User Prompts moving along the bakery conveyor belt toward the Pickup Window (🛎️).
• Robot-Cat Kitchen Stations = Real-World AI Engineering Components (Fast LLMs, Context Caches, Input Guardrails, RAG Vector DBs, Deterministic Tool Callers, System-2 Reasoning Models, and Multi-Agent Sous-Chefs) that intercept and process prompts before they reach the customer!
• Win / Lose Condition: Every order baked by the right AI Chef Station earns 🪙 Star Coins! If an unbaked order, an ungrounded 👻 Hallucination Puff, or a sneaky 🌶️ Prompt Injection slips all the way to the Pickup Window, you lose a 💖 Cafe Reputation Heart!

🕹️ How to Play in 3 Easy Steps:

1. STEP 1 - Pick an AI Chef Station (Left Menu): Click a station card on the left sidebar (start with 🥐 Flash Pastry Oven for 🪙 60). Check the Order Forecast chips under the level story to see which prompt types are coming!
2. STEP 2 - Click Any [➕ Slot] Tile on the Map: Click one of the 8 pastel counter pads (Slot 1-Slot 8) beside the conveyor belt to place your Robot-Cat Chef. (Tip: Click a placed chef anytime to ⬆️ Upgrade its baking speed or 🧹 Refund it!)
3. STEP 3 - Click ▶️ Open Cafe (or ✨ Auto-Chef Blueprint):
   • Click ▶️ Open Cafe (or press Spacebar) to start the shift and watch your Robot-Cat Chefs bake!
   • Want to watch an instant demo first? Click the purple ✨ Auto-Chef Blueprint button in the top bar on any of the 7 Story Shifts and the game will automatically build the optimal Guardrail, Cache, RAG, and Subagent pipeline and run it for you!

💻 Open-Source Code

🛠️ How I Built It

I built Prompt Paws using an open-source, zero-dependency HTML5 Canvas 60 FPS simulation engine (game.js), a 7-shift progressive AI engineering curriculum (levels.js), and a procedural WebAudio synthesizer (audio.js), orchestrated via Antigravity and open agentic workflows.

1. Order Routing & Guardrail Sanitization (game.js)

Each station inspects passing orders on the winding bakery conveyor ribbon and only picks up tasks that match its architectural role. Notice how Spicy Prompt-Injection Jalapeños cannot be safely baked by normal ovens until a Guardrail Flour Sieve sanitizes them into safe croissants:

function evaluateKitchenArchitecture() {
  const lvl = window.CAFE_LEVELS[currentLevelIdx];
  const weights = lvl.orderWeights;
  const typesPlaced = Object.values(placedStations).map((s) => s.type);
  const alerts = [];

  if (weights.jalapeno && !typesPlaced.includes('guardrail_sieve')) {
    alerts.push({
      kind: 'warn',
      text: 'Prompt Injection Risk! Place a Guardrail Flour Sieve near Slot 1 or 2 to neutralize spicy raccoon orders!'
    });
  }
  if (weights.tart && !typesPlaced.includes('vector_garden')) {
    alerts.push({
      kind: 'warn',
      text: 'Hallucination Warning! Berry Tarts need a Vector Berry Patch (RAG) so chefs do not guess facts!'
    });
  }
  if (weights.boba && !typesPlaced.includes('tool_blender')) {
    alerts.push({
      kind: 'warn',
      text: 'Math/API Alert! Add a Tool-Call Boba Mixer to run deterministic calculations accurately.'
    });
  }
  return alerts;
}

2. Real-Time AI Architecture Linter ("Chef Whiskers" 🐱‍🍳)

Before you open the cafe doors, Chef Whiskers (Staff AI Pastry Architect) performs live static analysis on your placed stations and warns you about architectural bottlenecks:

function evaluateKitchenArchitecture() {
  const lvl = window.CAFE_LEVELS[currentLevelIdx];
  const weights = lvl.orderWeights;
  const typesPlaced = Object.values(placedStations).map((s) => s.type);
  const alerts = [];

  if (weights.jalapeno && !typesPlaced.includes('guardrail_sieve')) {
    alerts.push({
      kind: 'warn',
      text: 'Prompt Injection Risk! Place a Guardrail Flour Sieve near Slot 1 or 2 to neutralize spicy raccoon orders!'
    });
  }
  if (weights.tart && !typesPlaced.includes('vector_garden')) {
    alerts.push({
      kind: 'warn',
      text: 'Hallucination Warning! Berry Tarts need a Vector Berry Patch (RAG) so chefs do not guess facts!'
    });
  }
  if (weights.boba && !typesPlaced.includes('tool_blender')) {
    alerts.push({
      kind: 'warn',
      text: 'Math/API Alert! Add a Tool-Call Boba Mixer to run deterministic calculations accurately.'
    });
  }
  return alerts;
}

🌐 Why Open-Source Visual Sandboxes Matter

Open-source AI models, open agent harnesses, and inspectable system design sandboxes ensure that every developer - not just giant enterprises - can master safe, cost-efficient AI engineering. By making the entire simulation engine, curriculum (levels.js), and architecture blueprints open-source with zero complex build steps and $0.00 API token cost, any educator or developer community can fork Prompt Paws, add new open-weight model stations (like local Gemma 4 or Ollama nodes), and teach agentic reliability visually!