Most enterprise AI deployments today are a collection of smart silos. You have an agent that summarizes documents. Another that queries your CRM. A third that handles scheduling. Each one is useful. None of them know the others exist. Getting them to collaborate means writing custom integration code that breaks every time one of the underlying models or platforms changes.
Google’s Agent2Agent (A2A) protocol, introduced in April 2025 and handed to the Linux Foundation in June 2025, is the attempt to solve that at the standards level. Think of it as HTTP for AI agents: a common language that lets agents discover each other, authenticate, delegate tasks, share context, and report progress regardless of what framework built them or what cloud they run on.
How It Actually Works
A2A is built on three core primitives. Agent Cards are JSON metadata that describe what an agent does, what inputs it needs, and how to authenticate with it: essentially a capability manifest any other agent can read. Task Management gives agents a standardized way to delegate work, track long-running jobs (hours or days, not just seconds), and receive real-time status updates. Context Sharing lets agents pass relevant information between each other without exposing internal data the receiving agent does not need.
The protocol runs over HTTP/HTTPS with JSON-RPC, supports OAuth2, API keys, and mTLS for authentication, and as of version 0.3 (July 2025) also supports gRPC for high-performance deployments. It is designed to slot into existing enterprise infrastructure rather than require a new runtime.
Crucially, A2A complements Anthropic’s Model Context Protocol (MCP) rather than competing with it. MCP handles vertical integration: connecting an agent to a specific tool or data source. A2A handles horizontal integration: connecting agents to other agents. Most serious multi-agent architectures will use both.
What This Means for ISVs
For ISVs building on Google Cloud, A2A changes the architecture of what you can ship. Internally, if you run multiple specialized agents across your own platform (a research agent, a drafting agent, a QA agent, a deployment agent), A2A gives them a standard coordination layer. You stop writing bespoke glue code between them and start building a composable agent mesh that grows without every new addition requiring integration work.
The product angle is where it gets more interesting. If your software product includes AI capabilities, A2A means your agents can interoperate with your customers’ existing agent ecosystems, and with third-party agents from partners like Salesforce, SAP, and ServiceNow, all of whom are already building to the spec. Your product stops being an island and becomes a participant in a broader agent network your customers are assembling.
That is a real differentiator in enterprise sales. Procurement teams buying AI-powered software increasingly ask how it fits into their wider automation strategy. A product built on A2A has a concrete, standards-based answer to that question. One built on proprietary agent integration does not.
The Competitive Angle
Microsoft has its own agent interoperability work inside the Copilot ecosystem, but it is largely proprietary and optimized for Microsoft-to-Microsoft integration. AWS has multi-agent orchestration in Bedrock, but no open cross-vendor protocol. A2A under the Linux Foundation is the only vendor-neutral open standard with 50-plus partners already building to it. For ISVs who sell across cloud environments and need a story that works with any customer’s stack, that neutrality matters.