A2A: Google’s Agent2Agent Protocol Could Redefine AI Interoperability

As AI agents grow in sophistication and popularity, a quiet but powerful transformation is underway—one that could revolutionize how autonomous systems interact. At the center of this shift lies A2A, short for Agent2Agent, Google’s open-source communication protocol designed to help disparate AI agents work together, regardless of their underlying vendor or framework.

Introduced in April 2025, A2A aims to fix one of the most glaring gaps in enterprise AI: agent interoperability. While the ecosystem boasts dozens of frameworks and vendors, most AI agents today are still siloed. They operate within confined platforms, unable to communicate seamlessly with others. That’s a massive bottleneck in building truly autonomous, collaborative systems.

Despite a high-profile launch and backing from 50 major tech companies—including Salesforce, Atlassian, and LangChain—A2A has struggled to capture the public imagination. It’s a critical layer in the future of AI infrastructure, yet remains underappreciated, likely due to its low visibility and abstract utility for developers still working on single-agent prototypes.

This analysis explores what A2A is, why it matters, and what might be holding it back. It also looks at its comparison to other protocols like Anthropic’s MCP, the broader implications for AI system design, and what role A2A might play in the future of AI networking.

Key Insights on A2A

A2A Defined: A2A (Agent2Agent) is an HTTP-based protocol that allows autonomous AI agents to communicate, exchange tasks, and collaborate across platforms securely and efficiently.

The Problem It Solves: AI agents typically operate in isolation. A2A removes that limitation, making it easier to create interconnected agentic workflows across tools, vendors, and enterprises.

Agent Cards: Each agent publishes a manifest (agent.json) that outlines its capabilities, authentication method, and endpoint—similar to OpenAPI specs for microservices.

Task Lifecycle: A2A structures interactions around tasks that move through defined states (submitted, working, input-required, completed), enabling complex back-and-forth workflows.

Rich Communication: Messages and Artifacts in A2A can include structured data, images, PDFs, and JSON, allowing for multimodal, layered interactions.

Streaming & Push Support: With SSE and webhook integration, agents can send live updates, enabling asynchronous collaboration.

Developer Onboarding: A Python SDK and demo agents are available for easy prototyping. With a few commands, developers can launch two agents and see live interactions based on the spec.

Before A2A: Most systems relied on monolithic agents or hardcoded API handoffs. True peer-to-peer collaboration was rare and brittle.

Challenges: Adoption hurdles include developer skepticism, protocol fatigue, lack of killer demos, operational overhead, and absence of support from major players like OpenAI or Microsoft.

MCP vs. A2A: The two aren’t competitors. MCP governs how an agent uses external tools, while A2A governs how agents talk to each other. They’re complementary protocols.

Stack Positioning: A2A fits into the agentic stack as a communication layer above tools and models, enabling orchestrated behavior among agents from different frameworks.

Potential Use Cases:

Cross-agent customer support systems

Multi-agent enterprise task automation

Swappable agents through capability standardization

Federated collaboration across organizations

Human-AI mixed workflows using A2A as the mediator

Discovery and Indexing: A2A’s structure suggests the potential for Google to build an “Agent Web Index,” akin to a Google Search for AI agents.

What Undercode Say:

The potential of A2A is significant—but timing and perception matter. Here’s a deeper analytic breakdown of what this protocol could mean and why it hasn’t gone viral—yet.

1. Protocol Fatigue vs. Infrastructure Evolution

Many developers are tired of “yet another protocol.” Between OpenAI’s function calls, LangChain chains, and Anthropic’s MCP, A2A feels like one more spec to learn. But where those tools focus on single-agent interactions with tools, A2A uniquely enables agents to delegate and communicate with each other, a function no previous protocol adequately handled. This isn’t just a nice-to-have; it’s a structural necessity for building modular AI ecosystems.

2. A2A’s Design Mirrors the Early Web

HTTP and REST enabled a fragmented internet to coalesce. A2A attempts the same, not with humans and web pages, but with autonomous agents and capabilities. The Agent Card is the new robots.txt or OpenAPI schema. It’s simple but potent—discoverable, machine-readable, and actionable.

3. Where A2A Will Thrive First

Enterprise ecosystems with multiple AI vendors—think Fortune 500 firms using Salesforce, ServiceNow, and custom Python agents—will benefit most. These companies already understand service meshes and microservice topologies. A2A speaks their language: distributed, discoverable, authenticated communication.