The Linux Foundation has launched the Agentic AI Foundation (AAIF) with contributions from Anthropic, Block, and OpenAI to standardize AI agents and prevent incompatible products. Based in the open-source community, the initiative aims to foster interoperability through shared protocols and frameworks donated at launch.
AI systems are advancing from chatbots to action-oriented agents that interact with tools and data. The Linux Foundation established AAIF as a neutral hub for open-source projects focused on these agents. This setup addresses the risk of fragmentation where proprietary systems lock users into specific ecosystems. Initial donations form the core of AAIF’s resources. Anthropic provided its Model Context Protocol (MCP), which standardizes connections between models, agents, tools, and data sources. Block contributed Goose, an open-source agent framework used internally for various tasks. OpenAI donated AGENTS.md, a straightforward instruction file that developers add to repositories to guide AI coding tools’ behavior. These elements serve as foundational components for building compatible AI agents.
Members joining AAIF at inception include AWS, Bloomberg, Cloudflare, and Google. Their participation indicates broad industry commitment to developing shared standards. This collaboration ensures that AI agents operate reliably across different platforms and vendors. The foundation’s structure supports ongoing development of protocols that enable seamless integration, reducing the need for custom solutions in every project.
OpenAI engineer Nick Cooper described protocols as a shared language for agents and systems. He stated, “We need multiple [protocols] to negotiate, communicate, and work together to deliver value for people, and that sort of openness and communication is why it’s not ever going to be one provider, one host, one company.” This perspective highlights how standardized protocols eliminate redundant integration efforts by developers. Cooper emphasized that diverse protocols allow for flexible collaboration among various AI components from different providers.
Jim Zemlin, executive director of the Linux Foundation, outlined the foundation’s objectives during launch discussions. He said, “By bringing these projects together under the AAIF, we are now able to coordinate interoperability, safety patterns, and best practices specifically for AI agents.” Zemlin contrasted this approach with closed proprietary stacks, where connections, behaviors, and orchestration remain confined to limited platforms. The AAIF coordinates efforts to establish uniform methods for agent interactions, ensuring consistent safety measures and operational guidelines across implementations.
Block, the fintech firm operating Square and Cash App, entered the AI infrastructure space through Goose. AI tech lead Brad Axen explained its internal use by thousands of engineers each week for coding, data analysis, and documentation. Axen noted that open-sourcing Goose demonstrates the framework’s capability to rival proprietary agents in large-scale environments. He told TechCrunch, “Getting it out into the world gives us a place for other people to come help us make it better.” Axen added, “We have a lot of contributors from open source, and everything they do to improve it comes back to our company.” By donating Goose to AAIF, Block gains exposure to community testing and enhancements. The framework integrates with shared standards like MCP and AGENTS.md, exemplifying the foundation’s goal of modular agent development.
Anthropic’s donation of MCP targets the protocol layer for AI integrations. MCP provides a standardized method to link AI models to external tools, data, and applications, avoiding the creation of numerous custom adapters. David Soria Parra, co-creator of MCP, shared the aspiration for widespread use. He told TechCrunch, “The main goal is to have enough adoption in the world that it’s the de facto standard.” Parra continued, “We’re all better off if we have an open integration center where you can build something once as a developer and use it across any client.” Transferring MCP to the Linux Foundation ensures neutral stewardship, preventing dominance by any single vendor. This move aligns with AAIF’s focus on agent standards, distinct from the foundation’s existing projects like PyTorch, Ray, and Kubernetes, which cover broader AI and developer tools.
OpenAI’s AGENTS.md functions as a simple configuration file placed in code repositories. It instructs AI coding assistants on permissible actions and behaviors within that codebase. This tool promotes predictable interactions between AI agents and development environments, facilitating safer and more efficient automation. Combined with MCP and Goose, AGENTS.md contributes to a cohesive set of building blocks for agent ecosystems.
AAIF operates under a directed fund model, where membership dues from companies support its activities. Zemlin clarified that financial contributions do not confer control over project directions. Technical steering committees determine roadmaps and priorities. This governance model maintains openness and merit-based decision-making. The Linux Foundation’s experience with major projects informs AAIF’s structure, emphasizing community-driven evolution over corporate mandates.
Indicators of AAIF’s effectiveness include the adoption rate of its standards and their integration into vendor agents globally. Zemlin described an early success metric as “the development and implementation of shared standards being used by vendor agents around the world.” For Cooper, ongoing progress involves active refinement. He expressed, “I don’t want it to be a stagnant thing. I don’t want these protocols to be part of this foundation, and that’s where they sat for two years. They should evolve and continually accept further input.” These measures track how protocols like MCP and frameworks like Goose influence real-world AI deployments.
Even under open governance, a particular implementation might emerge as predominant due to rapid deployment or high usage. Zemlin referenced historical precedents in open source. He cited Kubernetes, which gained prevalence in container orchestration through superior performance and adoption rather than vendor imposition. Zemlin stated that “dominance emerges from merit and not vendor control.” This dynamic benefits developers and enterprises by minimizing custom connector development, standardizing agent behaviors in codebases, and easing deployments in secure settings.
The foundational projects—MCP for connections, AGENTS.md for instructions, and Goose for frameworks—aim to create an ecosystem where AI agents combine modularly. This parallels the interoperable technologies that formed the basis of the modern web, allowing components from various sources to function together without proprietary barriers. AAIF’s efforts concentrate on agent-specific orchestration, safety protocols, and integration standards to support this interconnected environment.
