How Google’s A2A Protocol and Anthropic’s MCP Are Shaping AI Agent Interoperability

Google A2A Protocol Overview

At Google Cloud Next 25, Google announced the open‑source Agent‑to‑Agent (A2A) protocol, dubbed the "AI HTTP" standard, which aims to eliminate information silos between AI agents.

The protocol enables agents built with different vendors or frameworks to communicate, exchange information securely, and coordinate actions across enterprise platforms and applications.

Imagine a programmer whose personal AI assistant can fill logs in the corporate OA system, assist with code reviews, or converse with agents on other cloud platforms to complete tasks—A2A makes this possible.

More than 50 companies have joined the effort to make A2A a universal inter‑operation language for agents.

10 years ago Google launched Kubernetes, establishing a container orchestration standard.

Over a decade ago Google collaborated with W3C on HTML5, aligning browsers.

A2A Core Functions

1. Capability Discovery: Agents publish a JSON‑formatted "agent card" describing their abilities, allowing clients to select the best agent for a task and communicate via A2A.

2. Task Management: Communication is task‑oriented. The protocol defines a task object and its lifecycle; tasks may complete instantly or be tracked over time. Outputs are called artifacts .

3. Collaboration: Agents exchange messages to share context, replies, artifacts, or user commands.

4. User‑Experience Negotiation: Each message contains parts , self‑contained data blocks (e.g., generated images). Clients and remote agents negotiate the appropriate format for UI capabilities such as iframes, video, or web forms.

Real‑World Example: Recruitment Workflow

1. Recruiting: A hiring manager assigns an agent to find candidates matching the job description, location, and skills. The agent collaborates with specialized agents to gather candidates from multiple sources.

2. Interview: After recommendations are received, the manager can instruct the agent to schedule interviews, streamlining the selection process.

3. Background Check: Once interviews conclude, another agent can be invoked to perform background verification.

Anthropic MCP Protocol Overview

The Model Context Protocol (MCP) is an open‑source standard from Anthropic designed to integrate large language models (LLMs) with external data sources and tools, establishing a secure bidirectional connection.

Key features include a client‑server architecture where hosts can connect to multiple servers, standardized requests containing semantic intent, automatic service selection, and structured context packages returned to the LLM.

MCP also supports resource integration, allowing servers to combine enterprise knowledge graphs, document stores, and databases with fine‑grained RBAC access control, dynamic knowledge retrieval, provenance generation, and real‑time permission verification.

A2A vs. MCP: Positioning and Use Cases

Purpose: A2A focuses on agent‑to‑agent collaboration and communication, acting like a social platform for AI agents. MCP concentrates on connecting LLMs with external tools and data, serving as an instruction set for AI to invoke APIs.

Application Scenarios: A2A is suited for multi‑agent coordination in smart manufacturing or smart cities. MCP excels in intelligent customer service or Q&A systems where LLMs need to query knowledge bases or databases.

Relationship: The two protocols are complementary. In a smart‑medical system, MCP can link an LLM to medical records, while A2A enables diagnostic and treatment agents to cooperate.

Conclusion

As AI technology advances, the collaboration and communication between AI agents become increasingly critical. Both A2A and MCP play indispensable roles in driving the future of interoperable, efficient AI ecosystems.