Google's Agent2Agent Protocol: Not everything needs be over MCP anymore!

A deep dive into how Google's new A2A protocol could transform the way A/ agents work together across systems and platforms

The Dawn of Agent Collaboration

In a move that may fundamentally reshape AI system interactions, Google has launched the Agent2Agent (A2A) protocol — a standardized framework designed to enable seamless communication between Al agents across different platforms, tools, and companies.

While this announcement may have flown under the radar for many, it represents a critical infrastructure development for the evolving landscape of Al agents. As we transition from single-model Al systems that simply respond to prompts toward collaborative networks of specialized agents, the need forstandardized communication protocols becomes increasingly vital.

What is Agent2Agent (A2A)?

A2A is Google's open standard protocol that enables Al agents to communicate, coordinate and collaborate across diverse systems and platforms. Unlike previous approaches to agent collaboration which were often custom-built, fragile, and difficult to scale, A2A provides a standardized framework designed with security, asynchronicity, and real-world applications in mind.

Key features of the A2A protocol include:

• Agent-native workflow design: Optimized for how agents actually work without requiring shared memory or tight coupling

• Built on familiar standards: Leverages HTTP, Server-Sent Events (SSE), and JSON-RPC that

developers already understand

• Support for long-running tasks: Enables asynchronous operations and real-time updates

• Built-in security. Security considerations are foundational rather than added as an afterthought

• Cross-modal compatibility: Works with text, audio, video, and other modalities

A2A vs. MCP: Understanding the Difference

It's important to distinguish A2A from Google's Model Context Protocol (MCP). These are complementary rather than competing standards addressing different aspects of AI functionality:

• MCP (Model Context Protocol) focuses on empowering individual models with everything they need — context, tools, memory — to perform their specific functions effectively. Think of it as optimizing a single agent's capabilities and thought processes.

• A2A (Agent2Agent) is concerned with enabling multiple agents to work together as a coordinated team. It provides the communication infrastructure that allows different agents to collaborate, delegate tasks, and orchestrate complex processes.

In simple terms, MCP helps one intelligent model think clearly, while A2A helps multiple specialized agents work together efficiently without descending into chaos.

The Enterprise Focus of A2A

A2A appears to be designed with enterprise-scale applications in mind. Its comprehensive approach suggests Google is targeting organizations building internal networks of Al agents that need to collaborate securely and reliably within complex corporate environments.

This enterprise focus may mean that A2A isn't necessarily a lightweight solution that startups will immediately adopt. However, this deliberate design choice acknowledges the reality that for Al agents to move beyond impressive demonstrations into mission-critical business applications, robust infrastructure is essential.

Why A2A Matters for the Future of AI

The significance of A2A lies in its potential to solve one of the most challenging aspects of creating truly useful multi-agent systems: standardized, secure communication.

In the current Al landscape, we've seen remarkable demonstrations of individual Al capabilities, but orchestrating multiple specialized agents to work together seamlessly remains difficult. Each integration typically requires custom development, creating technical debt and scalability challenges.

A2A aims to solve this by providing 'better pipes" — the standardized communication infrastructure that enables agents to collaborate effectively regardless of which company developed them or where they're deployed.

The Road Ahead for Agent2Agent

As with any new protocol, A2A's success will ultimately depend on adoption. While Google has significant influence in the Al ecosystem, widespread implementation will require buy-in from other major players and the broader developer community.

The good news is that A2A aligns with the direction the industry is clearly moving: toward collaborative, specialized Al systems rather than monolithic models trying to do everything. As organizations deploy more AI agents across their operations, the need for standardized communication protocols becomes increasingly apparent.

Conclusion: Building the Foundation for an Agent-First World

Google's Agent2Agent protocol may not generate the same headlines as the latest large language model or Al assistant, but it represents something equally important the infrastructure that will enable the next generation of Al applications.

As we move toward an agent-first world where specialized Al systems collaborate to solve complex problems, protocols like A2A are what will make this vision technically feasible at scale, It's not the most flashy development in AI, but it may prove to be among the most consequential.

The launch of A2A signals that we're entering a new phase of Al development — one focused not just on individual agent capabilities but on creating the standards and infrastructure needed for these agents to work together effectively. For organizations planning their Al strategy, this development deserves close attention.

What are your thoughts on Google's Agent2Agent protocol? How do you see multi-agent systems evolving in your industry? Share your perspectives in the comments below.