How Google’s Agent2Agent protocol could transform business

In my last 25 years of building products with technology, I’ve learned one truth as a CTO: the most profound innovations aren’t the ones that dazzle us with flash and spectacle, but those that quietly reshape how our systems connect. I implemented this principle with early web services, with cloud infrastructure, and now I’m preparing for it again with Google’s Agent2Agent protocol—a development that might seem like just another API spec at first glance but carries the DNA of business transformation.

This week, I watched several Google AI agent demonstrations, including Agent2Agent (A2A). Dr. Fran Hinkelman, Developer Relations Engineering Manager at Google Cloud, took the stage to show off their Agent Development Kit (ADK). She explained that an effective agent needs three critical components: 1) instructions to define the agent’s goal, 2) tools to enable performance, and 3) a model to handle the LLM’s tasks. What caught my attention wasn’t just the individual agents but how they communicated with each other—a choreography of digital specialists working in harmony.

The silent language gap

In software architecture meetings, I often think of the United Nations headquarters and their translation system. Skilled translators in glass booths convert languages in real-time so diplomats can communicate effectively. Without them, the assembly would be brilliant minds talking past each other.

This is precisely the problem we face as CTO’s with our artificial intelligence deployments today.

As a technology leader, I’ve invested time and capital in remarkable AI systems that write emails, analyze supply chains, and optimize operations. But I’ve watched them operate like delegates without translators—brilliant in isolation, yet unable to share their insights. The content workflow AI we deployed can’t talk directly to our marketing operations teams AI without manual oversight. Our recruitment AI that sources candidates can’t coordinate with our scheduling system to book interviews.

The consequence? Team members become expensive human middleware, manually ferrying information between isolated AI systems—exactly the kind of repetitive work I deployed AI to eliminate in the first place.

Breaking babel

Google’s Agent2Agent (A2A) protocol is essentially building a universal translator for artificial intelligence. While the technical details are complex, the core idea is beautifully simple: create a standard way for AI agents to communicate, regardless of who built them or where they operate.

This isn’t just a Google project. It’s backed by over 50 major companies including Salesforce, SAP, Atlassian, Accenture, and Deloitte. This coalition suggests we’re witnessing not just a product launch but the birth of a new standard—like HTML for the web or USB for hardware.

Consider what happens in your own workplace when departments don’t communicate effectively. Marketing creates campaigns without consulting sales. Product development builds features customers haven’t asked for. Now imagine if every digital tool in your company not only performed its specialized task but could actively coordinate with others.

The orchestra effect

What makes an orchestra powerful isn’t simply having the best individual musicians—it’s how they listen and respond to each other. A virtuoso violinist playing alone creates beautiful music, but seventy talented musicians playing in harmony create something transcendent.

A2A could create this harmony for AI systems. Suddenly, your business isn’t running a collection of solo performers but a coordinated ensemble.

Take customer service in an organization. Currently, resolving a complex issue requires our representatives to access multiple systems—checking order status in one platform, verifying inventory in another, processing returns in a third, and updating customer records in our CRM. I bet if you calculate it that 40% of your time is spent context-switching between tools. With A2A, these separate AI tools become an ensemble under my technology strategy, passing information seamlessly. The customer inquiry agent verifies identity, then “speaks” directly to our inventory system about order status, which in turn coordinates with our shipping agent on delivery options—all in seconds, without human intervention. This isn’t theoretical—it’s a direct path to operational efficiency that I can measure on my KPI dashboard. The promise may become a reality this time. We have heard this story before, but previous decades past we didn’t have powerful LLM’s to work with.

The liberation of expertise

There’s another dimension to this that reminds me of how specialized knowledge became democratized through the internet. Before Google Search, expertise was siloed—locked away in specific people, departments, or resources. Search engines made that knowledge accessible to anyone.

A2A does something similar for artificial intelligence. Instead of your business being locked into one vendor’s ecosystem of AI tools, you gain the freedom to assemble your own dream team. Perhaps you prefer Google’s language model, Amazon’s inventory management, and a startup’s innovative customer analytics. Under the old paradigm, these would be impossible to integrate seamlessly. With A2A, they become colleagues rather than competitors.

You can see how Google’s Agent2Agent (A2) can also work with Anthropic’s Model Context Protocol (MCP) proposal that was released late last year, and now we’re seeing some compelling early projects appear. In my technical evaluations, we’ve found that OpenAI is adding support for Anthropic’s Model Context Protocol across its products, including the ChatGPT desktop app, which signals a major industry shift toward standardization. Microsoft has also partnered with Anthropic to create an official C# SDK for the protocol, expanding its reach into enterprise environments.

This kind of cross-vendor collaboration has profound implications for innovation. Small companies with breakthrough AI in specific domains can now plug their solutions into existing business workflows. The barrier to adoption drops dramatically when new tools can immediately communicate with your established systems.

Real-world reimagined

I’ve always believed that technology is most powerful not when it creates entirely new activities but when it transforms how we conduct existing ones. Email didn’t invent correspondence; it reimagined it. Smartphones didn’t invent photography; they revolutionized when and how we capture moments.

A2A won’t invent business processes, but it will fundamentally change how they operate. Imagine:

• Supply chains where inventory agents communicate directly with manufacturing agents, which talk to shipping agents, which coordinate with weather prediction agents to optimize routes—all without human micromanagement.
• Healthcare systems where diagnostic agents collaborate with treatment recommendation agents, which coordinate with scheduling agents to arrange follow-ups.
• Financial services where fraud detection agents alert transaction processing agents in real-time, preventing suspicious activities before they complete.

In each case, the individual AIs already exist. What’s missing is their ability to collaborate—the difference between individual musicians and a symphony.

The human elevation

There’s a persistent fear that AI advancement means human obsolescence. My research consistently shows the opposite: when routine tasks are automated, human work becomes more meaningful, not less necessary.

A2A accelerates this shift. When AI systems handle not just isolated tasks but their coordination, humans are freed to focus on truly human contributions: creativity, ethical judgment, emotional intelligence, and strategic thinking.

The businesses that thrive in this new era won’t be those that replace humans with AI, but those that elevate humans by delegating the appropriate tasks to AI. When your team no longer spends hours transferring information between systems or performing routine checks, they can invest that time in innovation and relationship-building.

The strategic roadmap

As a CTO planning my technology strategy, I’m well aware that A2A isn’t yet fully deployed—Google expects the complete rollout later in 2025. But I’ve already directed my team to prepare. The questions I’m asking my direct reports to investigate are:

1. Which of our processes involve multiple AI systems that currently don’t communicate?
2. Where are we paying skilled employees to serve as “human middleware,” manually transferring information between systems?
3. What small-scale A2A proof-of-concepts could we build this quarter to validate the business case?

I’ve lived through the early days of the web, the cloud transition, and the mobile revolution. In each case, companies that adapted early captured disproportionate market share. A2A represents a similar inflection point for AI architecture. The market leaders won’t be those who wait until the implementation playbook is complete, but those who recognize the pattern early and position their technology stack accordingly.

The universal language of AI is being standardized. As a technology leader, I’m already writing my adoption strategy. The question isn’t if your organization will join this conversation revolution, but whether you’ll be leading it or playing catch-up.