Rethinking Maintainability: Are We Still Writing Code for Humans?

As AI agents begin to participate in code maintenance and development, our long-standing definition of software maintainability, centered on human comprehension, must evolve. The future of maintainable code will balance readability for humans with structure optimized for machines.

Rethinking Maintainability: Are We Still Writing Code for Humans?

When we talk about code quality, we often rely on well-established criteria: suitability, efficiency, compatibility, usability, reliability, security, maintainability, and portability. Among these, maintainability has always stood out as a fundamentally human developer concern. It’s about how easy it is for a developer to understand, modify, fix, or extend the code. But what happens to this concept when AI agents start reading, understanding, and even changing code on their own?

With AI code assistants, and increasingly autonomous AI agents that can submit pull requests, refactor legacy code, or resolve bugs—the very notion of “writing code for humans” is being disrupted. If a machine can analyze, understand, and rewrite complex systems, do we still need to follow conventions meant for human eyes? What does maintainable code look like in a world where both humans and AIs are collaborators?

The Human-Centric Past

Traditionally, maintainable code meant:

• Clear and descriptive naming conventions
  
  
• Minimal complexity and tight cohesion
  
  
• Thorough documentation and inline comments
  
  
• Modular structure with consistent design patterns
  
  
• Predictability and adherence to coding standards
  
  

These practices help reduce the cognitive load on future developers. They make it easier to trace bugs, understand business logic, or onboard new contributors.

But humans are slow. We read line by line. We get confused by inconsistent naming. We debate over whether a function should be 20 or 50 lines long. That’s why maintainability has always emphasized clarity and simplicity for people.

The Machine-First Shift

Enter AI. Today’s AI agents don’t “read” code like we do. They parse abstract syntax trees (ASTs), understand dependency graphs, and can reason over entire codebases at once. More importantly, they don’t get tired, they don’t forget naming conventions, and they don’t need inline comments, at least not in the way humans do.

In this context, new ideas about maintainability are beginning to emerge:

• Consistency over clarity: AI tools prefer highly predictable, standardized code, even if it's verbose or redundant. Example: Write in boring, repetitive, boringly boring code. AI will reward you for it.
  
  
• Metadata-rich structures: AI agents benefit from structured annotations, type hints, and machine-readable documentation. Example: the more metadata you give, the better AI understands intent and constraints.
  
  
• Modular decomposition for reusability: Not because it’s easier for a junior dev to understand, but because it allows the AI to swap, rewrite, or test components more efficiently. Example: Even if it adds boilerplate, modularize aggressively.
  
  
• Style uniformity: Uniform syntax and structure optimize AI comprehension, even across languages and frameworks. Example: Use consistent formatting tools (Prettier, Black, clang-format, etc.) and style guides.
  
  

These shifts suggest that maintainability may soon be measured not by human intuition, but by machine efficiency.

A New Dual Standard?

This doesn’t mean humans are out of the loop. Even as AI takes on more coding tasks, we’re still the architects, reviewers, and ultimately the ones responsible for high-level decision-making. So instead of a complete overhaul, we might be heading toward a hybrid standard of maintainability:

• Code must be readable to humans and processable by AI.
  
  
• Documentation should include both natural language explanations and machine-readable metadata.
  
  
• Patterns and frameworks should balance developer accessibility and machine predictability.
  
  

How Should Developers Adapt?

To stay ahead, developers should begin thinking about machine collaboration as part of code design. That means:

• Embracing strict linting and formatting standards
  
  
• Using self-descriptive, metadata-rich code practices
  
  
• Writing tests and documentation not only for human QA, but for AI-based tools to interpret
  
  
• Learning to collaborate with AI agents as reviewers, co-authors, or even maintainers
  
  

Final Thoughts

The definition of software maintainability is expanding. Where we once asked “can a person understand and modify this?”, we now have to ask “can an AI improve this?” The answer might reshape everything from how we write functions to how we design entire systems.

In the near future, maintainable code won’t just be code a human can understand. It will be code that humans and machines can evolve together. And that may just be the biggest shift in software development since the invention of version control.