ai agent configuration best practices for 2026

AI coding agents have gone from novelty to necessity. Most teams now use at least one — Claude Code, Cursor, GitHub Copilot, or something newer. But here’s the thing: the quality of your agent’s output depends almost entirely on the quality of your configuration.

Great instructions produce great code. Vague instructions produce code you’ll spend twice as long fixing. Let’s talk about how to get this right, no matter which tool you’re using.

1. Start with what makes your project unique

Every AI model already knows how to write JavaScript, deploy to AWS, or set up a React component. You don’t need to teach it fundamentals. What it doesn’t know is the stuff that makes your project different from every other project:

Your naming conventions
Your architectural patterns
Your team’s preferred libraries
Your deployment pipeline quirks
The mistakes your last three PRs had to fix

Focus your agent configuration on project-specific context. That’s where the real leverage is.

2. Be concrete, not aspirational

There’s a big difference between instructions that sound nice and instructions that actually change behavior. Compare these:

Vague: “Write high-quality, maintainable code.”

Specific: “Keep functions under 25 lines. Use descriptive names — getUserByEmail not getUser. Handle all error cases explicitly with try/catch.”

The first one makes you feel good. The second one actually works. Every instruction in your configuration should be something you could objectively verify in a code review.

3. Show, don’t just tell

AI agents learn faster from examples than from rules. If you want your agent to follow a specific pattern, show it once and it’ll replicate it consistently.

Instead of writing “use our standard API response format,” include a short example:

// API responses always follow this shape:
type ApiResponse<T> = {
  data: T | null;
  error: string | null;
  metadata: { requestId: string; timestamp: string };
};

One concrete example is worth ten sentences of description. This applies to every platform — CLAUDE.md, .cursor/rules, and .github/copilot-instructions.md all benefit from embedded examples.

4. Structure your instructions in layers

Don’t dump everything into one giant wall of text. Organize your configuration into clear sections:

Project context — what is this codebase, what does it do
Tech stack — frameworks, languages, key dependencies
Conventions — coding style, naming, file organization
Commands — how to build, test, lint, deploy
Boundaries — what the agent should avoid doing

This structure helps the AI prioritize information. It also makes your configuration easier for humans to read and update — which matters more than you’d think.

5. Define what NOT to do

Positive instructions tell the agent what you want. Negative instructions prevent the mistakes that waste the most time. Think about recurring code review issues and write them down:

Don’t use any type — use unknown and narrow
Don’t create god components over 200 lines
Don’t make direct database calls from route handlers
Don’t use synchronous file operations in the server

“Avoid” lists prevent entire categories of problems before they happen.

6. Keep it concise

Every AI tool has a context window. Your agent configuration eats into that window, leaving less room for the AI to reason about your actual code.

The sweet spot is usually 200-500 lines of configuration. If yours is growing beyond that, ask: is every line earning its place? Cut anything redundant, obvious, or rarely relevant.

7. Iterate based on actual output

Your first draft won’t be perfect. The best agent configurations are evolved, not designed.

If the AI keeps making the same mistake, add a rule. If it handles something well without being told, remove that instruction and save context window space. Review your config monthly and cut rules that aren’t pulling their weight.

An AI agent that follows your conventions for every developer on the team is transformational. Commit your configuration files to version control. When a new developer joins, their AI assistant immediately knows how the team works — and when someone discovers a better instruction, the whole team benefits from a single PR.

9. Don’t fight the platform — use the right format

Each AI tool expects its instructions in a different format and location:

Claude Code reads CLAUDE.md at your project root
Cursor uses .cursor/rules with .mdc files
GitHub Copilot reads .github/copilot-instructions.md

Translating between these formats manually is tedious and error-prone. Your instructions drift apart. One platform gets updated, the others don’t.

This is exactly the problem spaget solves. Build your agent configuration once in a visual interface, then export to every format from a single source of truth. When you update your configuration in spaget, every export stays in sync.

No account required. Just head to app.spaget.ai and start building.

The bottom line

The gap between a poorly configured AI agent and a well-configured one is enormous. It’s the difference between an intern who needs constant supervision and a senior teammate who just gets it.

Invest in your agent configuration. Be specific. Show examples. Keep it concise. Iterate. And if managing configs across multiple tools sounds exhausting, let spaget handle the busywork so you can focus on building.

The best AI agent is the one that already knows how your team works before you type a single prompt.