Link & Synopsis
Link:
Synopsis:
Grant Slatton, former senior engineer at AWS S3 and founder of Row Zero (the world’s fastest spreadsheet), writes in this article a methodology for building complex software systems by:
- Starting with toy programs to understand constraints
- Beginning at the top of the stack (UI/API)
- Implementing in layers with minimal logic per layer
- Using stubs and mocks strategically
Context
I’m particularly interested in how teams balance software engineering rigor and AI development flexibility.
As AI systems grow in complexity, the principles for building robust software become increasingly relevant.
Traditional software engineering has developed patterns for managing complexity over decades, and AI Engineers should apply these patterns to AI system development.
Grant Slatton’s article presents an approach to building complex software that resonates particularly well with AI system development, where we often need to coordinate multiple models, handle states, and manage complex interactions.
His experience building high-performance systems at AWS S3 and Row Zero provides valuable insights for AI Engineers facing similar complexity challenges.
Let’s explore how to map software engineering principles to AI system engineering.
Key Implementation Patterns
The article outlines several approaches to complex software development that map well to AI system development:
- Top-Down Development
- Start with the desired AI system interface/API
- Define how users/other systems will interact
- Stub out lower-level components
- Refine implementation layer by layer
- Layer-Based Architecture
- Each layer handles minimal logic
- Clear separation of concerns
- Well-defined interfaces between layers
- Delegation to specialized components
- Strategic Use of Mocks
- Mock only IO-dependent components
- Use simple stubs during development
- Implement real components iteratively
- Focus on interface design first
These patterns from traditional software development provide a strong foundation for building complex AI systems, though their application requires careful consideration of AI-specific challenges.
Strategic Implications for AI Systems
For technical leaders building AI applications:
- Development Strategy
- Begin with the user interaction/user experience (UI/UX) layer
- Define clear model interaction patterns
- Start simple, add complexity gradually
- Focus on system architecture before implementation
- Implementation Approach
- Build working prototypes with stub responses
- Gradually replace stubs with real AI models
- Test integration points early
- Maintain flexibility for model changes
- Resource Management
- Defer expensive model development
- Test system flow with simpler models
- Validate architectural decisions early
- Optimize resource usage incrementally
To translate these strategic considerations into practical development practices, teams need a clear framework for implementation.
Implementation Framework
For teams building complex AI systems:
- Start with System Design
- Define top-level API/interface first
- Map out major system components
- Identify AI model integration points
- Plan data flow between components
- Insert eval staging points
- Consider when human input is needed
- Implement Incrementally
- Begin with stub AI responses
- Replace stubs with simple models
- Add sophisticated models gradually
- Maintain a working system throughout
- Focus on Interfaces
- Design clear component boundaries
- Define model input/output contracts
- Plan for model versioning
- Build robust error handling
Several key considerations emerge as teams apply this framework to real-world AI engineering systems.
Key Takeaways for AI Engineers
Important considerations when building complex AI systems:
- Architecture Patterns
- Apply traditional software layering
- Separate model logic from business logic
- Define clear integration points
- Build testable components
- Development Strategy
- Start high-level, work downward
- Use stubs for rapid prototyping
- Test with simple models first
- Validate system flow early
- Quality Management
- Test at multiple levels
- Verify component interactions
- Monitor model performance
- Build comprehensive test / eval suites
While these patterns are theoretically clear, their real value becomes apparent when considering practical experience.
Personal Notes
Having built both traditional software systems and AI applications, I’ve noticed that many teams try to start with the AI models / AI Agents first.
I still catch myself doing that, which is why I wanted to share this article (as a reminder to you and me).
We need to balance the excitement of AI capabilities and diving straight into development with systematic engineering practices.
This bottom-up approach often leads to the same problems the article describes: I/you end up with powerful components that don’t quite fit together properly.
Instead, treating AI models as implementation details of a well-designed system leads to more robust and maintainable applications.
Looking Forward: The Evolution of AI System Architecture
Engineering non-deterministic AI systems is more complex than engineering deterministic systems, so the principles of software engineering that were hard-won have become increasingly crucial.
The future of AI engineering will likely mirror the evolution of traditional software development, with established patterns and practices for managing complexity.
Teams that apply these software engineering principles early will build reliable, maintainable, and scalable AI systems.
These patterns will become fundamental to AI engineering, helping bridge the gap between experimental AI projects and production-ready systems.
Teams starting AI projects today would do well to embrace these proven software engineering principles from the start.