_Disclaimer: These are my personal thoughts and observations, based on my experience working with AI tools and modern development practices.
_
Since 2008, when Robert C. Martin released Clean Code, developers have lived by a few golden rules:
- "Clean code speaks for itself"
- Avoid comments — just write better code
- Tests should be self-explanatory
- Use small functions and abstract aggressively
These ideas made a lot of sense — for humans. They were born in an era where the main audience of your code was another developer reading it line-by-line.
But here's the shift -
We're no longer writing code just for humans.
We're writing it for AI agents, too...
Protocols like Model Context Protocol (MCP) and Agent-to-Agent (A2A) communication are pushing AI beyond "just chat". These protocols aim to give machines enough structured knowledge and context to reason about systems, interact with them, and even modify or debug code on your behalf.
But there's a catch:
❌ No comments in tests?
An AI agent won’t know why the test exists or what edge case it’s trying to guard against.
❌ Missing documentation?
Humans might guess what a class or module does. AI doesn’t. Without docs, APIs become black boxes.
❌ Over-abstracted “clean” code?
Machines struggle when logic is split across many files and layers. Too many indirections = confusion.
🧩 Why Human-Friendly Documentation Matters (Now More Than Ever)
MCP-based agents rely on structured inputs like:
- Project metadata
- Dependency trees
- API definitions
- Comments and contextual cues
- Workspace structure and environment configs
The more context we give them — via thoughtful naming, docs, comments, and structure — the more useful and accurate these agents become.
Want better docs using AI? Create better docs now!
It’s time to evolve our standards:
✅ Add useful comments (especially in tests, APIs, configs)
✅ Write clear, intentional documentation — not just auto-generated stuff
✅ Avoid unnecessary abstractions — prefer clarity over cleverness
✅ Think in "AI-first readability" terms: is this code understandable
with limited context?
🧠 Ask Yourself:
🤔 Can a machine or junior dev understand what this code is doing?
🤔 If my service were exposed via MCP — is there enough context for it to be useful?
🤔 Would an AI agent be able to safely refactor or debug this logic?
🤔 Does my abstraction actually simplify things — or just hide complexity?🔁 Bottom Line:
Clean Code helped us write better software for teams.
But in the AI-powered era — where models interact, debug, generate, and reason via protocols like MCP and A2A — we must level up.
Don’t throw away the old rules. Adapt them.
Write for humans and machines
Your future AI teammates will thank you 🤖❤️👨
