NIHAR MALALI

Google’s Agent2Agent (A2A) protocol addresses one of the biggest challenges in the agentic AI space: interoperability. As organizations build agents across different platforms, frameworks, and vendors, these agents need a standard way to discover each other, communicate, negotiate capabilities, and collaborate on tasks. A2A is Google’s answer — an open protocol for agent-to-agent communication.

The Interoperability Problem

Today’s agentic AI landscape is fragmented. An agent built with Amazon Bedrock cannot natively communicate with an agent built on Azure AI Agent Service. A LangChain agent cannot discover or delegate to a Semantic Kernel agent. Each framework has its own tool definitions, message formats, and execution patterns. This fragmentation limits the potential of multi-agent systems to single-vendor ecosystems.

How A2A Works

A2A defines a standard communication layer between agents, regardless of their underlying framework. The protocol specifies how agents advertise their capabilities through Agent Cards (JSON metadata describing what an agent can do), how agents send tasks to each other, how they stream progress updates, and how they handle multi-turn negotiations. Agents communicate over HTTP with JSON payloads, making the protocol easy to implement across any technology stack.

Key Protocol Concepts

• Agent Cards — Machine-readable descriptions of an agent’s capabilities, inputs, outputs, and authentication requirements. Think of them as API documentation for agents.
• Tasks — The fundamental unit of work. A client agent sends a task to a remote agent, which processes it and returns results. Tasks support streaming for long-running operations.
• Parts and Artifacts — Rich content exchange including text, files, structured data, and tool invocation results.
• Push Notifications — Agents can subscribe to updates from other agents, enabling asynchronous collaboration patterns.

A2A vs. MCP (Model Context Protocol)

A common question is how A2A relates to Anthropic’s Model Context Protocol (MCP). They solve different problems and are complementary. MCP standardizes how agents connect to tools and data sources — it is about agent-to-tool communication. A2A standardizes how agents communicate with other agents — it is about agent-to-agent collaboration. An agent might use MCP to access a database tool and A2A to delegate a sub-task to a specialist agent.

Enterprise Implications

For enterprise architects, A2A opens the door to vendor-diverse agent ecosystems. You can build specialized agents on the platform that best suits each use case — a data analysis agent on Databricks, a document processing agent on AWS, a customer-facing agent on Azure — and have them collaborate through the A2A protocol. This avoids vendor lock-in while enabling sophisticated multi-agent workflows.

The A2A protocol is still evolving, but its vision of interoperable AI agents is compelling. Organizations should monitor this standard closely and consider designing their agent architectures with protocol-level interoperability in mind, even before widespread A2A adoption.