Authors: @vigneshvrt , @karthick_shanmugam_256cf4
When we first rolled out Augment, the AI coding assistant, our team was genuinely excited. The promise was simple: pair programming with an AI that never gets tired.
In practice, the first few weeks were noisy.
We started seeing pull requests filled with well-meaning but unnecessary abstractions. Helper classes we didn’t ask for. Over-engineered solutions to simple problems. At times, it felt like the AI was trying to be the best developer in the room when all we needed was a focused teammate who listened.
That’s when we realized something important: what truly unlocks AI coding agent's potential isn’t additional data, but well-designed guardrails.
AI Agents Don’t Misbehave, They Misinterpret
When you give instructions to a human teammate, they infer intent. You might say “refactor this function,” and they’ll understand that you don’t want the entire module rewritten.
AI coding agents, on the other hand, optimize for probability, not intention. Without boundaries, they’ll often “help” in unexpected ways, expanding scope, adding excessive comments, or importing libraries that didn’t exist before.
So instead of blaming the AI, we started asking a different question:
What would happen if we trained the AI not on data, but on discipline?
Building the Rules for AI Pair Programming
We wrote down a lightweight set of principles for Augment, our “AI coding guidelines.”
These weren’t about style preferences; they were about alignment.
Each rule taught Augment how to work with us, not just for us.
Over time, we noticed that some guidelines made a huge difference in quality, while others played a supporting role. So we organized them into four tiers based on their real-world impact.
Tier 1: Foundational — The Big Levers
These directly control how predictable, trustworthy, and aligned the AI feels.If you only implement a few, start here.
1. Stay on task
“Only do what I ask. Don’t add extra features, assumptions, or changes.”
This rule stopped most of the chaos.
It prevented scope drift, where the AI would rewrite an entire file just because it wanted to be helpful.The moment we enforced this, output became predictable and reviews became faster.
2. Think in small steps
“Break work into clear, minimal micro-tasks.”
Instead of dumping entire modules, Augment started producing digestible, reviewable snippets just like a good engineer does during pull requests.
3. Collaborate, don’t override
“Treat this as pair programming. Suggest improvements, but let me drive.”
This made a cultural difference. The AI stopped taking over and started pairing. It restored trust.
4. Fail fast and visibly
“If something is impossible or inefficient, point it out instead of guessing.”
Early on, silent guesses caused debugging nightmares. This rule flipped that dynamic. A clear “I’m not sure” from the AI saves more time than a wrong answer ever could.
5. Keep code clean and minimal
“Write concise, readable, and maintainable code. Avoid unnecessary abstractions.”
This was our north star for readability. The AI stopped trying to be clever and started writing like a teammate who respects the next reader.
Tier 2: Quality and Consistency
These shape how pleasant it is to work with AI output, the difference between “usable” and “delightful.”
6. Avoid verbosity
Keeps code concise and readable, especially in verbose languages like Java or TypeScript.
Verbose code makes the AI look smart but slows everyone else down.
7. Adapt to my coding style
The AI should match existing naming, formatting, and idioms.
It makes AI-written code blend seamlessly into the repo, which builds human trust.
8. Follow best practices by default
The simplest way to prevent footguns.
Without this, the AI might use unsafe defaults or outdated patterns. With it, you get a baseline of sanity.
Tier 3: Contextual and Environmental
These matter deeply depending on your systems and domain.
9. Prioritize performance
Especially critical in low-latency or high-throughput environments.
We deal with near-real-time systems, so performance isn’t optional. This guideline ensures the AI considers cost per millisecond, not just correctness.
10. No hidden dependencies
Don’t import libraries or APIs unless I approve it.
AI tools love convenience imports. This rule prevents version mismatches, build issues, and hidden complexity from creeping in.
11. Keep cost in mind
A small but surprisingly powerful one.
For teams handling telemetry or observability, we found that reminding the AI about high-cardinality metrics saved real dollars. The AI doesn’t know your cloud bill, you do.
Tier 4: Lightweight and Context-Specific
Useful, but less critical in day-to-day co-coding.
Think of these as nice-to-haves for polish and efficiency.
12. Limit comments
Comments should clarify why, not what. This rule keeps the AI from writing essays for every function.
13. Ask for clarification when needed
Most AIs can’t literally pause and ask, but encoding this as “summarize assumptions first” works well in practice.
What Changed
We didn’t track metrics; we tracked friction.
Before these guidelines, reviewing AI code felt like babysitting, unpredictable and draining.After, it felt like pair programming again: fast, focused, and occasionally delightful.
Pull requests became smaller.
Code felt more human.
And the AI stopped trying to “help too much.”
We didn’t make Augment smarter (it already is). We made it disciplined.
The Takeaway
AI coding agents are like junior engineers with superpowers; fast, tireless, but literal.
Guidelines don’t limit them; they channel them.
By setting clear expectations, we turn probabilistic generators into reliable teammates.
If you’re using Cursor, Kiro, Copilot, Codeium, or Augment, try this experiment:
Write your own coding manifesto.
Feed it to your AI assistant before every session.
You’ll be surprised how much smarter the model becomes, not because it learned more, but because it finally understood your rules of the game.
You can find AI coding guidelines here on GitHub: [GitHub link]
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