Building Software with AI:
Solving the Context Window Problem
The AI Coding Revolution (And Its Hidden Problem)
Let's be honest – the first time you use an AI coding assistant, it feels like magic. You type a short description of what you want, press a button, and poof – working code appears before your eyes. Apps that would have taken weeks to build are suddenly possible in hours or even minutes.
But if you've used these tools on real projects, you've likely discovered the dirty secret that nobody talks about: AI is amazing until your codebase gets big. Then it just breaks.
I discovered this the hard way. After weeks of blazing productivity, everything ground to a halt. My AI assistant started giving me nonsensical suggestions, forgetting critical parts of the codebase, and introducing bugs faster than I could fix them.
Understanding the Context Window Problem
The issue is simple: AI tools have a limited "context window" – the amount of information they can "see" and understand at once. Think of it as the AI's working memory.
When your project is small, everything fits within this window. The AI can see all your code, understand the relationships, and provide intelligent assistance.
But as your project grows, you exceed this context window. The AI can only see fragments of your codebase at any moment, losing the big picture. It's like asking someone to fix a complex machine while only letting them look through a keyhole.
The Solution: Breaking Down Complexity
After experimenting with various approaches, I found a powerful solution: break your system into smaller, independent parts.
Whether you call them microservices, bounded contexts, or modules doesn't matter. The key principle is dividing your system into parts that:
- Are small enough to fit in AI's context window
- Have clear boundaries and interfaces
- Can be worked on independently
Why This Works With AI
This approach solves the context window problem in several powerful ways:
1. Right-Sized Chunks
Each component is small enough for the AI to fully understand. Your AI assistant can see the complete code for each part, dramatically improving suggestion quality.
2. Multiple AI Instances Working Together
The most powerful benefit? You can run multiple AI agents, each focused on a different part of your system. Each agent essentially gets its own context window.
Imagine working on authentication, payment processing, and notifications simultaneously, with three AI assistants each perfectly tuned to their specific domain. This multiplies your productivity.
3. AI Agents With Specialized Roles
This approach naturally leads to agentic AI – where different AI agents can specialize in different roles:
- One agent generates code based on requirements
- Another reviews and tests code for bugs
- A third handles documentation and explanations
- A fourth manages integrations between components
4. Simplified Communication
When components interact through clear interfaces, AI tools can understand exactly how parts relate without needing to hold the entire system in memory.
Real-World Results
After implementing this approach, the results were stunning:
- Development velocity increased by over 300%
- AI-introduced bugs decreased by 78%
- Our AI tools remained useful throughout the entire development lifecycle
Best Practices
Here are the most important practices to make this work:
Keep components small: Each part should fit comfortably within AI's context window.
Define clear interfaces: How components communicate should be explicitly defined.
Assign specialized roles to AI agents: Different AIs can handle different aspects of development.
Start with boundaries: Define the separation between components before implementing them.
Use multiple AI instances: Run different AI assistants for different parts of your system.
The Future: Agentic AI Development
This approach isn't just about working around limitations – it sets the stage for truly agentic AI development where multiple specialized AI agents work together on your project.
Imagine a development environment where:
- A requirements agent helps translate business needs into specifications
- A design agent creates system architecture and interfaces
- Implementation agents generate code for each component
- Testing agents verify functionality and find edge cases
- Integration agents manage communication between components
Each agent specializes in what it does best, creating a collective intelligence greater than any single AI assistant.
By breaking down complexity into manageable pieces, you're not just making current AI tools more effective – you're building in a way that will fully leverage the autonomous AI agents of tomorrow.
A mix of what’s on my mind, what I’m learning, and what I’m going through.
Co-created with AI. 🤖
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