AI coding assistants are everywhere. They live in our terminals, they're built into our IDEs, and they've fundamentally changed how we write software. From Codex and Copilot to Gemini and Claude, developers now have a powerful new collaborator.

But, there's a problem. To be effective, these AI agents need context. They need to understand your project's architecture, coding standards, and specific rules. This guidance lives in configuration files, but every agent speaks a different language.

You might have a CLAUDE.md for Claude, a .cursor/rules/ directory for Cursor, and .github/copilot-instructions.md for Copilot, and a JULES.md for Google Jules. This fragmentation creates a digital Tower of Babel in your repository. When you switch tools or collaborate with a team using different agents, you're stuck translating the same core instructions into multiple formats.

What if there was a universal translator? A single, standardized file to provide context to any AI coding agent?

That’s the idea behind AGENTS.md: an open standard for guiding AI coding agents.

What We'll Cover

• What is AGENTS.md? The "README for AI."
• Why Do We Need It? Taming the chaos of configuration files.
• What Goes Inside? A practical guide to crafting your own AGENTS.md.
• Making It Work Today: Bridging AGENTS.md with your current tools.
• The Future is Unified: What's next for AI-native development.

What is AGENTS.md?

Think of it this way: README.md is the front door for human developers. It's the first place you look to understand a project's purpose, setup, and contribution guidelines.

AGENTS.md is the front door for AI agents. It’s a single, standardized markdown file in the root of your project where AI assistants can get all the context they need to become a high-performing teammate.

This simple idea is already gaining traction. With support from tools like Codex, Cursor, and Gemini CLI, AGENTS.md has been adopted by over 40,000 open-source projects.

Why AGENTS.md? The Case for a Universal Standard

Developers are already feeling the pain of config fragmentation. On GitHub, users of tools like Claude Code and Cline have opened issues asking for a unified standard, specifically pointing to AGENTS.md.

Here’s the problem without a standard:

• Tool Lock-in: Switching to a new, better AI agent means rewriting your project's context from scratch in a new format.
• Team Friction: When your team members use different agents, your repository gets cluttered with redundant config files (.cursor/, claude.md, gemini.md), all containing slight variations of the same information.
• Maintenance Nightmare: Every time a rule changes—like updating your deployment command or linting standard—you have to update it in multiple places, hoping you don't miss one.

AGENTS.md solves this by creating a single source of truth.

Instead of a tangled mess of agent-specific files, you have one clean, universal file that works across all tools, which can come with its own drawbacks.

Why you might want to use multiple AI code assistant configuration files

Using multiple configuration files for an AI code assistant can offer a few benefits, most noteably is that of allowing for specialization, consistency, and tighter control in more complex projects.

Improved project-specific customization

• Tailored behavior: A configuration file can be set up for a specific project or microservice to guide the AI with project-specific settings. This ensures the AI understands the unique context, coding style, and framework of that codebase, providing more accurate and relevant suggestions.
• Contextual awareness: For large codebases, AI assistants perform better when the code is organized into modular files. This allows the AI to process each file's context more effectively without being overloaded, leading to faster and more accurate suggestions.
• Optimized performance: You can create lightweight configurations for smaller tasks or projects that don't require the AI to have a deep understanding of the entire codebase. This can reduce processing time and resource consumption.

Enhanced flexibility and control

• Switching models: By having multiple configurations, you can easily switch between different AI models, like Claude, GitHub Copilot, or a self-hosted model, to determine which is best for a specific task. This prevents vendor lock-in and allows you to always use the most effective tool.
• Experimentation: Developers can experiment with different prompts, settings, and AI models by creating separate, isolated configuration files. This allows for testing and fine-tuning without disrupting the main project workflow.

Streamlined team collaboration

• Consistent guidance: A team can share a standard configuration file to ensure all members receive the same AI guidance. This helps enforce consistent coding practices, security rules, and tool usage across the entire development workflow.
• Centralized management: Centralizing API keys and other secrets within managed configurations allows for secure collaboration. Teams can roll out pre-approved AI models and workflows while retaining oversight of their data.
• Reduced inconsistency: A standard configuration file prevents inconsistent AI outputs that can arise when different team members use different settings or prompts, which is a major headache for Python projects and others

What's the difference between AI code assistant configuration files?

To improve the AI's relevance and prevent security issues, developers can explicitly control which parts of a codebase the AI should focus on or ignore.

• .aiignore: Similar to a .gitignore file, this file tells the AI which files and folders to exclude from its analysis. This is critical for security, as it prevents the AI from being exposed to sensitive information.
• .github/copilot-instructions.md: This file contains custom instructions specifically for GitHub Copilot, guiding its behavior and code generation according to project standards or user preferences.
• AGENTS.md: These files are associated with the concept of "agents" in AI coding, particularly with tools like GitHub Copilot's coding agent. They can define behaviors and instructions for these agents, potentially at a more granular level than the general copilot-instructions.md. AGENTS.md suggests a collection of agent definitions.
• CLAUDE.md: This file serves a similar purpose to copilot-instructions.md but is specifically designed for the Claude AI model, allowing users to provide custom instructions for its interactions and code generation.
• .cursorrules, .windsurfrules, .clinerules: These files, and their directory counterparts, contain configuration or rule sets for specific AI coding tools.

README.md: This is a standard Markdown file used in most software projects to provide an overview, instructions, and other essential information about the project. While not directly tied to AI assistant configuration, it can implicitly guide AI tools by providing context about the codebase.

The key distinction is between files designed to configure and instruct specific AI coding assistants (e.g., Copilot, Claude, Cursor, Windsurf) and general project documentation (README.md) that provides human-readable information about the project. The AI configuration files differ based on the particular AI tool they target and the specific instructions or rules they convey to that tool.

What Goes Inside an AGENTS.md File?

AGENTS.md consolidates the essential knowledge required to contribute to your project effectively. It's a living document that captures your team's conventions, architectural decisions, and operational knowledge.

Here’s a practical example of what a robust AGENTS.md might look like:

Start small, and let it grow. Your AGENTS.md doesn't need to be perfect on day one. Begin with the most critical information and expand it over time. Each time a developer (or an agent) learns something new about the project, add it to the file.

Bridging AGENTS.md with Existing Tools

While many modern agents support AGENTS.md out of the box, some older tools still look for their own native config files. For those, you can use two simple bridging strategies to get them to read your central AGENTS.md file.

Method 1: Symbolic Linking

A symbolic link (symlink) is a pointer to another file. You can create symlinks that trick agents into reading AGENTS.md while looking for their native file.

Open your terminal in the project root and run these commands for the tools you use:

For Claude Code
ln -s AGENTS.md CLAUDE.md
For Cursor
mkdir -p .cursor/rules

ln -s ../../AGENTS.md .cursor/rules/rules.mdc
For GitHub Copilot
mkdir -p .github

ln -s ../AGENTS.md .github/copilot-instructions.md

Your tools continue to work as expected, but now they all draw their context from a single source.

Method 2: Using Imports

Some agents support importing one markdown file into another. For example, in Claude Code's CLAUDE.md file, you can simply add a line to import your universal file:

This approach keeps your setup clean and ensures AGENTS.md remains the primary source of truth.

What's the difference between agents.md and prompt.md?

AGENTS.md and .prompt.md files serve different purposes in guiding an AI coding assistant. AGENTS.md provides general, project-level context, while .prompt.md files define reusable, task-specific instructions. A .prompt.md file defines a reusable, task-specific prompt that can be executed directly by an AI assistant. Purpose: Automate common, repeatable development tasks. A .prompt.md:

• Encapsulates complex tasks: Lets you define and reuse complex, multi-step instructions for specific jobs.
• Task specialization: Creates a specialized prompt for common tasks, such as generating a test case or scaffolding a component.
• Chat integration: Some AI assistants, like GitHub Copilot in VS Code, allow developers to run prompt files directly from the chat interface using a slash command.

Key differences summarized

|| Aspect || AGENTS.md || .prompt.md || | Scope | Project-wide | Task-specific | | Purpose | Defines general rules and project context for any task | Automates specific, repeatable tasks | | Trigger | Automatically referenced by the AI for every interaction | Manually invoked by the user, often via a chat command | | Content | High-level instructions, conventions, and setup details | Detailed instructions and examples for a single, focused task | | Analogy | A handbook for the AI | A macro or recipe for the AI |

What's Next: The Future of AI Collaboration

AGENTS.md is more than just a configuration file; it's a step toward a future where AI and human developers collaborate seamlessly.

Imagine a world where any AI agent can clone a repository and instantly understand its context, conventions, and goals. Onboarding a new AI assistant becomes as simple as pointing it to a URL. Open-source projects can accept high-quality contributions from autonomous agents because the rules of engagement are clearly defined.

This is the future that a common standard like AGENTS.md enables. For now, we can use simple bridges like symlinks to make it work. But as the ecosystem evolves, expect more and more tools to adopt AGENTS.md as the default.

One file to guide them all. One file to align them. One file to bring them all and in the codebase bind them.