A2a

The Linux Foundation launched the Agentic AI Foundation (AAIF) in December 2025 to provide neutral governance for the infrastructure powering AI agents in production. It now governs MCP, goose, and AGENTS.md — protocols and tools used across OpenAI, Anthropic, Google, and Block’s agent stacks. What Is the Agentic AI Foundation (AAIF)? The Agentic AI Foundation (AAIF) is an independent, vendor-neutral foundation under the Linux Foundation umbrella, established in December 2025 to govern open infrastructure for AI agent systems. AAIF launched with 150+ member organizations — making it the fastest-growing foundation in Linux Foundation history — and three anchor projects: the Model Context Protocol (MCP), goose (an open-source AI agent framework by Block), and AGENTS.md, a standardization spec for defining agent behavior. Co-founded by Anthropic, OpenAI, and Block, with backing from Google, Microsoft, AWS, Bloomberg, and Cloudflare, AAIF occupies the same structural role in the AI agent ecosystem that the Linux Foundation occupies for open-source operating systems: it removes any single company’s control over infrastructure that the entire industry depends on. The agentic AI market is projected to reach $42 billion by 2027 at a 47% CAGR, and AAIF’s founding reflects the industry’s recognition that production-grade AI agents need shared governance, not competing proprietary protocols. ...

OpenAgents is an open-source framework for building persistent AI agent networks — systems where agents continue to exist, learn, and collaborate long after an initial task completes. Unlike LangGraph or CrewAI, which treat agents as stateless task runners, OpenAgents gives every agent a durable identity, a shared workspace with a persistent URL, and native support for both MCP (Model Context Protocol) and A2A (Agent-to-Agent) protocols from day one. What Is the OpenAgents Framework? OpenAgents is an open-source Python framework designed specifically for building persistent, interoperable AI agent networks. Launched in early 2026, it addresses the fundamental limitation of most agent frameworks: agents disappear once a task finishes, losing all learned context. OpenAgents agents maintain a durable workspace accessible at a stable URL (e.g., workspace.openagents.org/abc123), enabling teams to bookmark a network and return to an evolved, context-rich system days or weeks later. The framework ships with three core components — Workspace, Launcher, and Network SDK — and natively implements both the MCP and A2A protocols, which means agents built with different underlying frameworks can collaborate without custom glue code. In 2026, as 85% of developers regularly use AI tooling, the demand for long-running, team-aware agent infrastructure has grown sharply, and OpenAgents is purpose-built to fill that gap. The key distinction from alternatives is its architectural commitment: persistence and interoperability are first-class features, not afterthoughts bolted on via plugins. ...

MCP (Model Context Protocol) handles agent-to-tool communication — giving an AI agent access to APIs, databases, and services. A2A (Agent-to-Agent Protocol) handles agent-to-agent communication — letting one AI agent delegate tasks to another. They solve different problems and production multi-agent systems increasingly use both. If you’re building with AI agents in 2026 and you’re confused about which protocol you need, you probably need both. Why AI Agents Need Standardized Protocols Before MCP and A2A, integration complexity for AI agents grew quadratically. Every agent needed custom code to connect to every tool, and every multi-agent system needed custom logic for agents to communicate. A team building an agent that used GitHub, Slack, PostgreSQL, and Stripe had to write and maintain four separate integrations. If they added a second agent that needed to delegate to the first, they’d write a fifth. With ten agents and ten tools, that’s potentially 100 integration points to maintain. ...