How A2a Improves AI Collaboration

How do we make AI agents truly useful in the enterprise? Right now, most AI agents work in silos. They might summarize a document, answer a question, or write a draft—but they don’t talk to other agents. And they definitely don’t coordinate across systems the way humans do. That’s why the A2A (Agent2Agent) protocol is such a big step forward. It creates a common language for agents to communicate with each other. It’s an open standard that enables agents—whether they’re powered by Gemini, GPT, Claude, or LLaMA—to send structured messages, share updates, and work together. For enterprises, this solves a very real problem: how do you connect agents to your existing workflows, applications, and teams without building brittle point-to-point integrations? With A2A, agents can trigger events, route messages through a shared topic, and fan out information to multiple destinations—whether it’s your CRM, data warehouse, observability platform, or internal apps. It also supports security, authentication, and traceability from the start. This opens up new possibilities: An operations agent can pass insights to a finance agent A marketing agent can react to real-time product feedback A customer support agent can pull data from multiple systems in one seamless thread I’ve been following this space closely, and I put together a visual to show how this all fits together—from local agents and frameworks like LangGraph and CrewAI to APIs and enterprise platforms. The future of AI in the enterprise won’t be driven by one single model or platform—it’ll be driven by how well these agents can communicate and collaborate. A2A isn’t just a protocol—it’s infrastructure for the next generation of AI-native systems. Are you thinking about agent communication yet?

Google just launched Agent2Agent (A2A) protocol that could quietly reshape how AI systems work together. If you’ve been watching the agent space, you know we’re headed toward a future where agents don’t just respond to prompts. They talk to each other, coordinate, and get things done across platforms. Until now, that kind of multi-agent collaboration has been messy, custom, and hard to scale. A2A is Google’s attempt to fix that. It’s an open standard for letting AI agents communicate across tools, companies, and systems, that securely, asynchronously, and with real-world use cases in mind. What I like about it: - It’s designed for agent-native workflows (no shared memory or tight coupling) - It builds on standards devs already know: HTTP, SSE, JSON-RPC - It supports long-running tasks and real-time updates - Security is baked in from the start - It works across modalities- text, audio, even video But here’s what’s important to understand: A2A is not the same as MCP (Model Context Protocol). They solve different problems. - MCP is about giving a single model everything it needs- context, tools, memory, to do its job well. - A2A is about multiple agents working together. It’s the messaging layer that lets them collaborate, delegate, and orchestrate. Think of MCP as helping one smart model think clearly. A2A helps a team of agents work together, without chaos. Now, A2A is ambitious. It’s not lightweight, and I don’t expect startups to adopt it overnight. This feels built with large enterprise systems in mind, teams building internal networks of agents that need to collaborate securely and reliably. But that’s exactly why it matters. If agents are going to move beyond “cool demo” territory, they need real infrastructure. Protocols like this aren’t flashy, but they’re what make the next era of AI possible. The TL;DR: We’re heading into an agent-first world, and that world needs better pipes. A2A is one of the first serious attempts to build them. Excited to see how this evolves.

The year is 2025 and we're finally at the point of needing a lingua franca for agents to speak to each other. 🗣️ At Cloud Next, Google introduced A2A (Agent-to-Agent Protocol) - an open standard for multi-agent communication. Think of it as a structured way for autonomous agents to talk to each other - negotiate tasks, pass messages, share capabilities, and coordinate complex workflows. Notably, this isn’t a replacement for MCP (Model Context Protocol), the protocol from Anthropic that allows an agent to access external tools and memory. They’re complementary: 🧰 MCP = tool access + context. It equips an agent to do things more effectively. Think of it as the data plane: what an agent can do. 🍳 It’s like giving a chef a well-stocked kitchen and recipe book - they can now whip up anything on the menu. 🤝 A2A = collaboration + coordination. It allows agents to work with each other. This is the control plane: who does what, when, and how. 🍽️ It’s like setting up a group chat between the chef, the delivery driver, and the party planner - so they can actually throw the dinner party. They are orthogonal to each other: MCP scales vertically (one agent gets smarter), A2A scales horizontally (many agents get connected). One builds capability. The other builds collaboration. Why does this matter? Because the future won’t be one giant omniscient model. It’ll be many specialized agents - planners, researchers, copilots, auditors - operating semi-autonomously, often across different vendors. And to avoid chaos, they’ll need to speak a shared language. A2A begins to define that language. 💡 A few implications: 1️⃣ Agent capability discovery becomes structured - agents can advertise what they’re good at. 2️⃣ Long-running workflows become first-class - with control-plane semantics for retries, notifications, and human-in-the-loop UX. 3️⃣ Modularity becomes viable - different models optimized for different tasks can now be composed more fluidly. The real unlock is the interoperability mindset - one where no single model does it all, but many can do more, together. Will it work? Probably not immediately. Enterprise standards rarely do. But like many things in enterprise software - XML, OAuth, capitalism - A2A may just stick around long enough to become inevitable. And, of course, the politics. A2A launched with 50+ partners like Atlassian, Box, Cohere but conspicuously absent from the list were OpenAI, Anthropic, Meta, or Microsoft. Which is notable because those are the companies whose agents would be… useful to talk to. But also understandable, because nobody wants to show up to someone else’s standards party unless they get to host. MCP has begun to unite warring factions and started getting early adoption - the question is whether A2A will see enough community support to become the orchestration standard. Regardless, AI seems to be firmly in its plumbing era. 🪠 ✨