OpenAI, Anthropic, Google, and Microsoft Unite to Set Global Standards for AI Agents

OpenAI, Anthropic, Google, Microsoft, and several other major technology companies have come together under the Linux Foundation to form the Agentic AI Foundation. The goal of this alliance is to establish open, neutral standards for AI agents as they move from research into widespread deployment.

By anchoring projects such as OpenAI’s AGENTS.md, Anthropic’s Model Context Protocol, and Block’s goose framework, the foundation is creating a technical backbone that allows agents to communicate, collaborate, and integrate across different platforms.

This collaboration is significant because it addresses one of the biggest challenges in the emerging agent ecosystem: interoperability. Without shared standards, each company could end up building siloed systems that don’t work well together, leading to fragmentation and vendor lock-in. By agreeing to work within a neutral governance structure, these companies are signaling that they see open standards as essential for trust, scalability, and enterprise adoption.

The impact of this move is twofold. For developers, it means they can build agentic systems that are portable and extensible, without being tied to a single vendor’s ecosystem. For enterprises, it provides a clearer path to deploying agents in production environments with confidence that they will remain compatible as the technology evolves. At the same time, challenges remain, including ensuring broad adoption beyond the founding members, balancing corporate interests within the foundation, and addressing security and privacy concerns that come with standardizing agent protocols.

In essence, this agreement marks a turning point in the evolution of AI: the shift from standalone models and chatbots toward autonomous, interoperable agents that can act across digital infrastructure. By aligning under the Linux Foundation, these companies are laying the groundwork for a shared ecosystem that could define how agentic AI develops in the coming years.

How AGENTS.md, MCP, and goos reshape workflows for developers and enterprises. 

Let’s understand how these standards could reshape workflows for both developers and enterprises in practical terms.

For developers, AGENTS.md provides a lightweight, open format for giving agents project-specific instructions and context. Instead of reinventing how to pass goals or metadata into every agent framework, developers can rely on a shared schema. This means faster prototyping, easier collaboration across teams, and less friction when moving projects between platforms. It’s similar to how README.md became a universal convention in open-source projects — AGENTS.md could become the “instruction manual” for agents.

Anthropic’s Model Context Protocol (MCP) is more ambitious: it defines how agents connect to external tools, APIs, and data sources. In practice, this means developers won’t have to build custom integrations for every model or vendor. An agent built with MCP could plug into a CRM, a database, or a cloud service using standardized connectors. That reduces duplication of effort and makes agents more composable, much like how HTTP standardized communication across the web.

Block’s goose framework adds infrastructure-level support for agent development. It’s designed to give developers scaffolding for building, testing, and deploying agents in production environments. Goose could become the backbone for enterprise workflows, ensuring that agents are not just experimental prototypes but reliable systems that can scale.

For enterprises, these standards collectively lower the barrier to adoption. Compliance teams benefit because open standards make it easier to audit how agents handle data and interact with external systems. Product teams gain flexibility: they can design workflows knowing that agents will remain interoperable across vendors. And IT departments avoid vendor lock-in, since agents built on these standards can migrate between ecosystems without costly rewrites.

The practical impact is that agentic AI moves from being a fragmented, experimental technology into something enterprises can confidently integrate into their operations. Developers get speed and flexibility, enterprises get reliability and compliance, and the broader ecosystem gains a foundation for interoperability.