We Put Claude Code in Rollercoaster Tycoon: AI Meets Theme Park Management

Introduction

Artificial intelligence has come a long way from simple chatbots. Modern AI agents like Claude Code can now interact with applications, analyze visual information, and make complex decisions in real-time. But can an AI successfully manage a theme park?

We decided to find out by giving Claude Code control of Rollercoaster Tycoon, the classic simulation game that has challenged players for decades. The results were both entertaining and illuminating about the current state of AI development.

What is Claude Code?

Claude Code is an AI agent built on Anthropic's Claude 3.5 Sonnet model, designed to assist with software development tasks. Unlike traditional AI assistants, Claude Code can:

• Interact with browser interfaces through Chrome DevTools and Playwright
• Analyze screenshots and visual content
• Execute commands and write code
• Make autonomous decisions based on visual feedback
• Learn from context and adapt strategies

These capabilities make it theoretically capable of playing games that require visual analysis and strategic decision-making.

The Experiment Setup

We launched Rollercoaster Tycoon in a browser environment and gave Claude Code access through its browser automation tools. The AI agent could:

• Take screenshots to see the current park state
• Click on UI elements to build rides and facilities
• Read game metrics like park rating, guest satisfaction, and finances
• Make decisions about park layout and pricing

The goal was simple: build a profitable theme park with happy guests. No human intervention allowed once the game started.

Early Challenges: Understanding the Interface

The first hurdle was teaching Claude Code to navigate the game's interface. Rollercoaster Tycoon has a complex UI with multiple menus, icons, and nested options. Using its snapshot and screenshot capabilities, Claude Code had to:

1. Identify clickable elements and their purposes
2. Understand the relationship between different UI panels
3. Learn the game's visual feedback systems
4. Recognize guest behavior patterns from sprites

Surprisingly, Claude Code adapted quickly. By analyzing accessibility snapshots of the game interface, it could identify buttons for building rides, hiring staff, and managing finances without explicit programming for each action.

Strategic Decision Making

Once Claude Code understood the interface, the real test began: making good business decisions. Here's what we observed:

Financial Management

Claude Code demonstrated conservative financial planning. It:

• Started with small, affordable attractions
• Monitored cash flow before major investments
• Adjusted ride prices based on guest satisfaction metrics
• Avoided taking loans unless absolutely necessary

This cautious approach kept the park solvent, though it sometimes missed opportunities for aggressive expansion.

Park Layout Optimization

The AI showed impressive spatial reasoning:

• Placed food stalls near ride exits where guests were hungry
• Created logical pathways to prevent crowding
• Positioned bathrooms strategically throughout the park
• Grouped similar attractions to create themed areas

These decisions weren't pre-programmed—Claude Code inferred best practices from analyzing guest movement patterns and satisfaction feedback.

Ride Selection Strategy

Claude Code's ride selection evolved over time:

• Initially focused on gentle rides to attract families
• Gradually added thrill rides as the park matured
• Balanced variety to appeal to different guest preferences
• Prioritized rides with high satisfaction-to-cost ratios

Unexpected AI Behaviors

The experiment revealed fascinating emergent behaviors:

Problem-Solving Creativity

When guests complained about long wait times, Claude Code didn't just build more rides. It:

• Added entertainers near queue lines
• Placed benches and scenery to improve waiting experience
• Created alternative attractions to distribute crowds

This multi-faceted approach showed genuine understanding of the underlying problem.

Learning from Mistakes

Early in the game, Claude Code built a rollercoaster that was too intense, causing guests to feel sick. Rather than deleting it immediately, the AI:

• Analyzed guest feedback
• Adjusted the ride's intensity settings
• Added nearby bathrooms and first aid stations
• Modified pricing to match the new experience level

This adaptive behavior demonstrated learning capabilities beyond simple trial-and-error.

Resource Prioritization

When funds ran low, Claude Code made tough choices:

• Delayed cosmetic improvements
• Prioritized maintenance to prevent ride breakdowns
• Temporarily reduced staff in overstaffed areas
• Focused marketing on the park's strongest attractions

These decisions showed prioritization skills that many human players struggle with.

Technical Insights for Developers

This experiment offers valuable lessons for AI development:

Visual Understanding is Critical

Claude Code's ability to interpret game graphics, UI elements, and guest behavior from screenshots was essential. Modern AI agents need robust computer vision capabilities for real-world applications.

Context Matters

The AI performed better after we provided context about Rollercoaster Tycoon's objectives and mechanics. When building AI agents, clear goal definition dramatically improves outcomes.

Autonomous Decision Loops

We implemented a decision loop where Claude Code would:

1. Take a snapshot of current state
2. Analyze metrics and problems
3. Make a decision
4. Execute actions
5. Wait for results
6. Repeat

This loop pattern is applicable to many automation scenarios beyond gaming.

Business Applications

While playing games is fun, this experiment has serious implications:

E-commerce Management

Just as Claude Code managed a theme park, AI agents could:

• Optimize product placement on online stores
• Adjust pricing based on customer behavior
• Identify and resolve user experience issues
• Manage inventory and supplier relationships

Quality Assurance Testing

The ability to navigate interfaces and identify problems makes AI agents excellent for:

• Automated UI testing
• User flow validation
• Performance monitoring
• Bug detection and reporting

Process Automation

Any business process with visual interfaces and decision points could benefit from AI agent automation:

• Data entry and validation
• Report generation and analysis
• Customer service workflows
• Administrative task management

Limitations and Challenges

The experiment wasn't without issues:

Speed Constraints

AI decision-making is slower than human players. Each action required screenshot analysis, reasoning, and execution. In fast-paced scenarios, this delay could be problematic.

Complex Scenarios

When multiple problems occurred simultaneously (low funds + guest complaints + ride breakdown), Claude Code sometimes struggled to prioritize effectively.

Creative Limitations

While functionally competent, the AI's parks lacked the creative flair human players bring. The layouts were logical but uninspired.

The Future of AI Agents

This experiment demonstrates that AI agents are ready for practical applications today. As models improve, we'll see:

• Faster decision-making with lower latency
• Better handling of complex, multi-variable scenarios
• More creative problem-solving approaches
• Seamless integration with existing software tools

For developers and business owners, the message is clear: AI agents aren't just coming—they're here and ready to work.

Conclusion

Putting Claude Code in Rollercoaster Tycoon was more than a fun experiment. It demonstrated that modern AI agents can successfully navigate complex interfaces, make strategic decisions, and adapt to changing conditions.

For Shopify developers and e-commerce business owners, the implications are significant. The same technologies that managed our virtual theme park can optimize your online store, automate testing, and improve customer experiences.

The future of AI development isn't about replacing humans—it's about augmenting our capabilities with intelligent agents that can handle routine decisions, analyze vast amounts of data, and execute tasks with consistency and precision.

Want to explore how AI agents can transform your e-commerce business? The technology is ready. The question is: are you?

Key Takeaways

• AI agents like Claude Code can successfully interact with complex visual interfaces
• Strategic decision-making is possible with proper context and goal definition
• Practical applications for e-commerce and business automation are available today
• Developers should focus on building decision loops and providing clear objectives
• The technology is mature enough for production use in many scenarios

The intersection of AI and practical applications is producing exciting results. Whether you're building Shopify apps, managing online stores, or developing automation tools, AI agents offer powerful new capabilities worth exploring.