When overseas R&D teams use Claude Code, Codex, and Cursor simultaneously to develop projects, they’ve been facing a major headache for over a year: maintaining two nearly identical sets of AI project manuals for the same project. If they updated `CLAUDE.md` but forgot to sync `AGENTS.md`, the AI would read the outdated specifications to generate code, resulting in discrepancies in coding style, project standards, and team requirements—and the cost of rework remained high. Now, this pain point plaguing developers worldwide is finally seeing a major turning point.

On September 18,Claude Code Version 2.1.277 has been released. Key update: If a CLAUDE.md file does not exist in the project directory, the system will automatically detect and read AGENTS.md. The AI programming community has finally taken a crucial step forward by adopting a common project description language.

What Is an AI Project Specification? The Origins of the Two Sets of Documents

CLAUDE.md and AGENTS.md are essentially project manuals written for AI agents to read; they serve as the project’s “AI version of an employee handbook.” Project compilation methods, test scripts, coding standards, and project constraints are all documented within these files. Each time an AI coding session is initiated, the AI first reads these documents and develops code in accordance with the team’s specifications.

The difference between the two lies in the target audience: CLAUDE.md is Anthropic A proprietary specification recognized only by Claude Code; AGENTS.md was introduced by OpenAI in August 2025 and is a general-purpose standard,Codex, Cursor, GitHub Copilot, Gemini CLI, and Devin all support it. Currently, over 60,000 open-source projects worldwide have integrated AGENTS.md, making it the de facto industry standard.

Overseas R&D Teams: Long Tormented by Synchronizing Two Sets of Documents

Cross-border R&D teams rarely use just a single AI programming tool. Some engineers prefer Claude Code, while others use Codex,Cursor Collaborative development. For the same project, two sets of AI specifications must be prepared. Document desynchronization is common; if an update is overlooked, the AI will generate code based on outdated specifications, resulting in inconsistent code quality.

Overseas developers have come up with all sorts of workarounds: creating symbolic links to files, including external files within documentation, and configuring session hooks to import document content. AI is supposed to help humans write code, yet humans end up spending their energy solving documentation compatibility issues between AI tools. On GitHub, a feature request calling for Claude Code to support `AGENTS.md` has garnered over 5,200 upvotes and more than 300 comments—far surpassing other requests in terms of votes—clearly demonstrating the urgent need among developers worldwide.

Interestingly, OpenAI and Anthropic were already working together under the same foundation. In December 2025, the Linux Foundation established the AI Agents Foundation (AAIF) to promote open standards for AI agents. OpenAI contributed AGENTS.md, while Anthropic contributed MCP Under the agreement, Google, Microsoft, and AWS are all Platinum members. While they sat at the same roundtable to co-develop open standards, their flagship products remained incompatible for a long time—a conflict that lasted 13 months has finally been resolved.

Four Major Trends: Understanding the Transformation of the AI Programming Ecosystem—Key Focus Areas for Teams Expanding Overseas

1. AI Programming Enters the Era of Open Ecosystems

Document interoperability, customization, and cross-tool compatibility are no longer just nice-to-have features—they are gradually becoming core capabilities of AI coding tools. Products that cling to closed ecosystems will gradually lose developers.

2. The Focus of AI Competition Shifts to the Battle Over Foundational Standards

Lightweight, open standards such as AGENTS.md and MCP determine whether multiple tools can work together; mastering these standards means controlling the ecosystem. When companies donate standards to foundations, it is not an act of philanthropy, but rather a move to secure control over the universal rules governing global AI infrastructure.

3. China’s AI sector has evolved from exporting products to exporting ecosystems

In the past, we’ve mostly seen domestic large language models and AI applications expanding into overseas markets; however, moves by DeepSeek to open-source the Harness framework—a low-level infrastructure component—signal that Chinese AI companies are beginning to participate in shaping global AI infrastructure standards, a trend worth tracking in the long term within the cross-border technology industry.

4. New Criteria for Selecting Overseas R&D Teams: Openness Becomes a Key Metric

AI programming agents are quickly becoming standard equipment for cross-border teams. When selecting a solution, the focus is no longer solely on the model’s coding capabilities,Is it compatible with industry standards, does it support customization, and is it vendor-neutral?...will be a key consideration. An interoperable AI toolchain significantly improves the efficiency of cross-team, cross-location, and cross-border R&D collaboration.

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