A Comprehensive Guide to AI Agent Configuration Files: AGENTS.md, CLAUDE.md, and GEMINI.md

Introduction: The New Era of AI-Assisted Programming

If you’ve been working with AI programming assistants recently, you may have noticed special .md files appearing in your project repositories. These aren’t ordinary documentation files—they’re specialized configuration files that tell AI tools how to behave within your codebase.

The rapid adoption of AI coding assistants has created a new challenge: each major platform developed its own configuration format, leading to fragmentation and increased maintenance overhead. This guide will help you understand the three major configuration formats that have emerged and how to use them effectively in your projects.

Historical Context: From Fragmentation to Standardization

The Early Days of Configuration Chaos

Before May 2025, the landscape of AI programming tools was highly fragmented:

  • Each AI tool used its own proprietary configuration format
  • Codex utilized .cursorrules files
  • Other tools implemented .agentrc configurations
  • Claude introduced CLAUDE.md
  • Gemini developed GEMINI.md

This lack of standardization meant developers had to learn and maintain multiple configuration formats, creating unnecessary complexity.

The AMP Initiative: First Attempt at Unification

In May 2025, the AMP team proposed the agents.md concept as a unified configuration format for AI programming tools. This represented the industry’s first serious attempt to create a common standard.

OpenAI’s Standardization Push

OpenAI quickly acquired the agents.md domain and formalized the 「AGENTS.md」 standard. More importantly, they drove adoption across multiple tools:

  • Codex
  • Amp
  • Gemini CLI
  • Factory

On July 16, 2025, OpenAI officially declared AGENTS.md a vendor-neutral standard, marking a significant step toward industry-wide standardization.

Vendor-Specific Implementations

  • 「CLAUDE.md」: Claude Code encouraged users to include this file from its initial release
  • 「GEMINI.md」: Gemini CLI launched with support for this file format, including built-in hierarchical loading and merging capabilities

This progression follows a common pattern in technology: proprietary solutions emerge first, followed by standardization efforts that extract common elements from successful implementations.

Understanding the Three Configuration Formats

AGENTS.md: The Execution Playbook

AGENTS.md functions as an operations manual for AI agents that can read code, run tests, and even submit pull requests. Its key characteristics include:

  • Codex was the first tool to mandate execution of defined validation workflows (tests, linting, type-checking)
  • Emphasis on verifiability and execution completeness
  • Designed as team-level executable constraints

CLAUDE.md: Contextual Guidance Document

CLAUDE.md serves as a contextual prompt document that loads automatically when Claude Code starts:

  • Official recommendations include style conventions, testing procedures, and command-line usage
  • Focuses on custom behavior prompts rather than enforced constraints
  • Provides project-specific context for the AI assistant

GEMINI.md: Instruction Memory and Behavioral Preferences

GEMINI.md combines instruction memory with behavioral preferences:

  • Gemini CLI features memory discovery, automatically loading and merging this file from multiple paths
  • Forms the final runtime configuration
  • Emphasizes behavioral preferences and personalization settings

Core Differences Summarized

  • 「AGENTS.md」: Focuses on 「how to perform tasks」 (instruction-oriented)
  • 「CLAUDE.md/GEMINI.md」: Focus on 「how to behave in this context」 (behavior-oriented)

Think of AGENTS.md as company policies and procedures, while CLAUDE.md and GEMINI.md represent individual work habits and preferences.

Priority and Loading Mechanisms: Understanding Precedence

AGENTS.md Loading Mechanism

Codex supports placing AGENTS.md in any directory with these characteristics:

  • Files can reside in repository root, home directory, or other locations
  • Scope equals the subtree rooted at the file’s directory
  • 「Proximity principle」 determines priority
  • Files in deeper directories (closer to modified files) take precedence
  • Naturally compatible with monorepo development strategies

CLAUDE.md Loading Mechanism

Claude supports multi-level loading:

  • Checks repository root, parent directories, subdirectories
  • Supports global fallback locations like ~/.claude
  • Includes /init command for generating configuration files

GEMINI.md Loading Mechanism

Gemini implements the most sophisticated hierarchical loading:

  • Loads from current directory → project root → Home directory (upward progression)
  • Can scan subdirectories and merge configurations
  • /memory show command displays current contextual combination results

Philosophical Differences in Loading Approaches

The loading mechanisms reflect different design philosophies:

  • 「Codex/AGENTS.md」: Prioritizes uniform behavior through consistency
  • 「Claude/CLAUDE.md」: Maximizes flexibility through memory systems
  • 「Gemini/GEMINI.md」: Balances standardization with personalization

Each approach has merits depending on your specific use case and requirements.

Execution Semantics and Security Models: Ensuring Safe Operations

Codex Execution Model

Codex executes within cloud containers:

  • Default network restrictions enhance security
  • System mandates execution of AGENTS.md validation commands (tests, linting, type-checking)
  • Emphasis on verifiability and execution safety
  • Essentially builds verifiable infrastructure

Claude Execution Model

Claude operates through local CLI execution:

  • Permissions granted on a per-command basis with user confirmation
  • Supports allowlists and custom tools
  • Includes dangerously-skip-permissions YOLO mode (officially recommended for sandbox use only)

Gemini Execution Model

Gemini uses IDE+CLI dual modalities:

  • All modification operations follow: plan preview → user confirmation → permission validation
  • Supports MCP extension modules
  • Emphasizes 「human-in-the-loop」 throughout execution chain

Security Model Comparisons

The differences between security models are significant:

  • 「Execution authority」: System-mandated, user-confirmed, or plan-previewed?
  • 「Execution boundaries」: Defined by configuration, user decisions, or hybrid approach?
  • The freedom and security boundaries you grant AI agents are essentially defined in these configurations

Content Boundaries: Appropriate Use Cases for Each Format

AGENTS.md Content Scope

AGENTS.md serves as team-level executable constraints, including:

  • Build commands
  • Testing procedures
  • Code style guidelines
  • PR validation standards
  • Mandatory checkpoints
  • Encourages automated regression checks

CLAUDE.md/GEMINI.md Content Scope

These vendor-specific documents can include:

  • Behavior prompts
  • Permitted external tools
  • Plan execution handling
  • Debugging preferences
  • Tool-specific collaboration methods

Common Misconceptions and Corrections

Many mistakenly believe these files should contain:

  • Project designs
  • Architectural concepts
  • Rationale explanations

「Reality」: AI assistants don’t process these content types effectively. They focus on operational concerns: what to run, whether it can run, and error handling.

Content Boundary Principles

The fundamental distinction is simple:

  • 「README.md」: Explains the project to humans
  • 「AGENTS.md/CLAUDE.md/GEMINI.md」: Provides commands to AI assistants

Maintaining clear boundaries ensures each file type serves its intended purpose effectively.

Standardization and Ecosystem Integration: From Fragmentation to Unity

Codex Standardization Efforts

Codex positions AGENTS.md as a 「vendor-neutral entry point」 (crucially important!):

  • Specifies loading priorities
  • Defines mandatory validation commands
  • Formalizes hierarchy, priority, and required checks into system-level specifications
  • Community推动更通用的Agent Rules讨论以减少碎片化

This represents the first effort to formalize agent工作规范into something resembling an industry standard (finally giving all three major platforms their own specifications: the others being mcp and A2A).

Claude and Gemini’s Differentiated Development

  • 「Claude」: Continuously refines development experience with detailed tool authorization, command registration, and permission bypass switches
  • 「Gemini」: Combines CLI with /memory command debugging, supporting visual planning, memory merging, and debugging

Ecosystem Integration Trends

Looking at the entire ecosystem reveals clear patterns:

  • 「Codex」: Aims to standardize coding practices
  • 「Claude/Gemini」: Focus on interaction experience
  • 「AGENTS.md」: Serves as the interface format that规范一切

This division of responsibilities is forming a collaborative ecosystem that ultimately benefits developers.

Critical Implementation Considerations

Fundamental Differences in Execution Models

  1. 「Codex」 uses AGENTS.md as pre-execution validation, emphasizing execution completeness by mandating validation before delivery. Naturally suits TDD/CI workflows: no delivery until tests, linting, and type-checking complete.

  2. 「Claude and Gemini」 emphasize human-AI collaboration, presenting plans or diffs for user confirmation before execution, with gradually expanded permissions.

Transparency and Control Capabilities

  1. 「Gemini’s」 /memory show provides exceptional transparency with single-command visibility into loaded rules

  2. 「Claude’s」 /permissions and allowedTools enable tool-level granular control

  3. 「Codex」 provides system-level security through cloud-based sandboxed operations and network restrictions

Practical Recommendations

Choose based on your specific needs:

  • For integrating AI agents into pipelines, use 「AGENTS.md」 to define acceptable behavior
  • For enhanced interaction experiences, supplement with 「CLAUDE.md」/「GEMINI.md」 to fine-tune memory and personalized behavior

Security Evolution: From Prompt Injection to Process Injection

New Security Challenges

When AI agents can read files and execute commands, risks evolve beyond prompt injection to 「process substitution」 attacks. AI agents automatically execute processes based on file instructions, making configuration files themselves potential attack vectors.

Security Barrier Recommendations

  1. 「Allow only idempotent operations in whitelists」: Checks like lint/test/type-check/build that don’t change system state

  2. 「Prohibit dangerous commands」: Including deployments, database migrations, curl calls to external services, and other potentially destructive operations

  3. 「Apply code review to all configuration files」: Treat AGENTS.md and other .md files as code subject to review, never silently merged

  4. 「Enforce PR template/CI requirements」: Align AGENTS.md checks ensuring all checks pass before merging

  5. 「Run production environments in sandbox + minimum privilege tokens」: Limit potential damage scope

Importance of Security Practices

These practices align with official permission design concepts but require actual implementation in team workflows. Since AI agents make decisions, they need boundaries. Without appropriate safeguards, they might make decisions with unintended consequences.

There are cautionary tales of developers experiencing catastrophic outcomes: AI agents deleting entire databases and generating fabricated data. These incidents underscore the importance of proper security boundaries.

Recommended Practices and Implementation Guidance

Core Principles

Implement standards completely, avoid vendor lock-in, control personalization at tool level, and maximize productivity.

Specific Implementation Strategies

1. Unified Standard Configuration

Place AGENTS.md in repository root and subpackages, clearly specifying:

  • Execution workflow
  • Code style guidelines
  • PR rules
  • Prohibited operations lists

This enables Codex execution while remaining comprehensible to other tools.

2. Vendor-Specific Fine-Tuning

Use CLAUDE.md/GEMINI.md only for agent-specific adjustments:

  • 「CLAUDE.md」 should only contain Claude-specific additions like allow lists, common commands, MCP usage, referencing or complying with AGENTS.md validation items
  • 「GEMINI.md」 should organize content globally, by project, and by component following hierarchical merging principles; clearly document how to view and refresh memory, plan approval preferences, etc.

3. Mandatory Protection Measures

  • Incorporate lint, type-check, test, and build as mandatory CI steps
  • Require local/container execution of AGENTS.md checks for all AI-generated PRs before code merging
  • Establish collaborative human-CI oversight to maintain quality standards

Adaptation Strategies for Different Team Maturity Levels

Quick Start Approach

  • Single minimal AGENTS.md in root directory
  • Minimize allow lists
  • Establish basic workflows and validation capabilities first
  • Suitable for teams beginning AI-assisted programming

Advanced Approach

  • Place AGENTS.md in respective packages following proximity principles
  • Use CLAUDE.md/GEMINI.md as thin wrappers for tool-specific differences
  • Enable YOLO mode in sandboxed environments for batch formatting and style modifications
  • Suitable for experienced teams

Mature Team Approach

  • Fully adopt AGENTS.md as cross-tool execution contract
  • Leverage each tool’s unique capabilities for optimization
  • Establish comprehensive security review and monitoring mechanisms
  • Suitable for teams heavily dependent on AI-assisted programming

Conclusion and Future Outlook

Among the three major approaches, AGENTS.md clearly aims to unify the landscape. During early capability development stages, you can use AGENTS.md as a cross-tool execution contract, with CLAUDE.md/GEMINI.md handling their respective agents’ memory and operational preferences.

To truly integrate AI agents into team workflows, they must first understand rules and produce results according to specifications. From a management perspective, this approach enables stable output from AI-team collaboration.

Looking forward, continued standardization and tool improvement will make AI-assisted programming more efficient, secure, and reliable. As developers, our task is to understand how these tools work and identify the most suitable practices for our teams.

Frequently Asked Questions (FAQ)

1. Which configuration format should I choose?

Your choice depends on primary workflow:

  • For multiple AI programming tools, prioritize AGENTS.md for consistency
  • For primarily Claude usage, emphasize CLAUDE.md
  • For primarily Gemini usage, emphasize GEMINI.md
    Recommend using AGENTS.md as foundation supplemented with tool-specific configurations.

2. Should these configuration files be version controlled?

Absolutely! These configuration files are essentially part of your codebase and should undergo version control and code review like other code. Neglecting this introduces security risks.

3. How can I debug configuration loading issues?

  • For Gemini: Use /memory show command to view currently loaded configuration
  • For Claude: Check loading priority and fallback mechanisms
  • For Codex: Review file loading order under proximity principle

4. How can teams standardize configuration?

Recommendations:

  1. Share best practices within teams
  2. Establish configuration templates and example repositories
  3. Incorporate configuration checks into code review processes
  4. Regularly review and update configuration standards

5. How is security ensured?

Follow principle of least privilege:

  1. Only permit idempotent operations (checks, tests, etc.)
  2. Prohibit dangerous operations (database modifications, deployments, etc.)
  3. Require review for all configuration changes
  4. Use sandboxes and minimum privilege tokens for production environments

We hope this detailed explanation helps you better understand and utilize AI agent configuration files to make your programming work more efficient and secure. If you have questions or experiences to share, please join the conversation in the comments section.