How A2a Improves Enterprise Workflows

🚨 𝗕𝗥𝗘𝗔𝗞𝗜𝗡𝗚: 𝗚𝗼𝗼𝗴𝗹𝗲 𝗹𝗮𝘂𝗻𝗰𝗵𝗲𝘀 𝘁𝗵𝗲 𝗔𝗴𝗲𝗻𝘁𝟮𝗔𝗴𝗲𝗻𝘁 (𝗔𝟮𝗔) 𝗽𝗿𝗼𝘁𝗼𝗰𝗼𝗹 — and it might just define the future of AI agent interoperability. Until now, AI agents have largely lived in silos. Even the most advanced autonomous agents — customer support bots, hiring agents, logistics planners — couldn’t collaborate natively across platforms, vendors, or clouds. That ends now. 🧠 𝗘𝗻𝘁𝗲𝗿 𝗔𝟮𝗔: a new open protocol (backed by Google, Salesforce, Atlassian, SAP, and 50+ others) designed to make AI agents talk to each other, securely and at scale. I’ve spent hours deep-diving into the spec, decoding its capabilities, and comparing it with Anthropic’s MCP — and here's why this matters: 🔧 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗔𝟮𝗔? The Agent2Agent protocol lets autonomous agents: ✅ Discover each other via standard Agent Cards ✅ Assign and manage structured Tasks ✅ Stream real-time status updates & artifacts ✅ Handle multi-turn conversations and long-running workflows ✅ Share data across modalities — text, audio, video, PDFs, JSON ✅ Interoperate across clouds, frameworks, and providers All this over simple HTTP + JSON-RPC. 🔍 𝗪𝗵𝘆 𝗶𝘀 𝘁𝗵𝗶𝘀 𝗵𝘂𝗴𝗲? 💬 Because agents can now delegate, negotiate, and collaborate like real-world coworkers — but entirely in software. Imagine this: 🧑 HR Agent → sources candidates 📆 Scheduler Agent → sets interviews 🛡️ Compliance Agent → runs background checks 📊 Finance Agent → prepares offer approvals ...and all of them communicate using A2A. 🆚 𝗔𝟮𝗔 𝘃𝘀 𝗔𝗻𝘁𝗵𝗿𝗼𝗽𝗶𝗰’𝘀 𝗠𝗖𝗣 — 𝗞𝗲𝘆 𝗗𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝗰𝗲𝘀 ✅ 𝘈2𝘈 (𝘎𝘰𝘰𝘨𝘭𝘦) 🔹 Built for agent-to-agent communication 🔹 Supports streaming + push notifications 🔹 Handles multiple modalities (text, audio, video, files) 🔹 Enterprise-ready (OAuth2, SSE, JSON-RPC) 🔹 Uses open Agent Cards for discovery ✅ 𝘔𝘊𝘗 (𝘈𝘯𝘵𝘩𝘳𝘰𝘱𝘪𝘤) 🔹 Focused on enriching context for one agent 🔹 No streaming or push support 🔹 Primarily text-based 🔹 Lacks enterprise-level integration 🔹 Not an interoperability standard 📣 Why I'm excited This is not just a spec. It's the HTTP of agent collaboration. As someone building systems at the edge of AI, agents, and automation — this protocol is exactly what the ecosystem needs. If you're serious about building multi-agent systems or enterprise-grade AI workflows, this spec should be your new bible. 📘 I wrote a deep technical blog post on how A2A works ➡️ Link to full blog in the comments! 🔁 Are you building multi-agent systems? 💬 How do you see A2A changing enterprise automation? 🔥 Drop your thoughts — and let’s shape the agentic future together. #AI #A2A #Agent2Agent #EdgeAI #Interoperability #AutonomousSystems #MCP #GoogleCloud #Anthropic

Google just launched something interesting in the AI space called A2A (Agent-to-Agent). It’s a framework where different AI agents can talk to each other, work together, and check each other’s work. Instead of one big model doing everything, A2A lets multiple smaller agents handle different tasks — like writing code, reviewing it, and deciding what to do next. Kind of like how real teams operate. What’s exciting here is that this is not just about breaking one prompt into parts (like MCP does). In MCP, you're still driving one model to do multiple tasks in a structured way — like giving it a checklist. But with A2A, you're creating actual independent agents, each focused on their own specialty, talking and collaborating like co-workers. It’s a more modular, flexible setup. Another interesting angle: A2A could enable lightweight agents on the edge (like inside your mobile app) to talk to more powerful agents running on the backend. That could mean faster responses, less data transfer, and better privacy — especially useful in customer-facing apps. In the customer onboarding space, this opens up a lot. You often need: One agent to recommend the right financial product Another to verify documents and extract data A third to assess customer risk With A2A, these specialized agents can be trained once and reused across different workflows — no need to build new agents or clunky rule-switching logic every time something changes. We’re exploring how this could help improve our own onboarding and document automation flows. Early days, but it feels like a solid step toward building smarter, more adaptable AI systems.

Our B2B processes are siloed. AI agents are siloed. Everything is...siloed. Google's new Agent-to-Agent (A2A) protocol changes everything. Here's why every B2B tech leader needs to understand A2A now: 1. It's the "TCP/IP of AI agents" - a universal language that lets agents built on different frameworks communicate seamlessly 2. Your Crew AI agents can instantly work with LangChain, Google ADK, or any A2A-compatible agent from other companies 3. B2B workflows that took days of emails can now happen in minutes - even when everyone's asleep 4. Imagine reducing vendor communication cycles from 48 hours to under 4 minutes using AI agents that speak the same protocol Think about this: When companies' AI agents can seamlessly collaborate 24/7, traditional B2B communication bottlenecks disappear. The A2A protocol handles everything through standardized HTTP endpoints and JSON cards, so implementation is surprisingly straightforward. Your agents expose their capabilities, and other agents discover and use them. We're day zero of this revolution, but the adoption curve will be steep. Companies implementing A2A now will have a massive competitive advantage within months.

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.

Architecture - SAP Agent2Agent (A2A) Interoperability Big news #SAPSapphire! SAP just dropped something big. Agent2Agent (A2A) collaboration is here - powered by SAP + Google + Microsoft + Amazon Web Services (AWS). This means intelligent agents can actually talk to each other across platforms. Solving real business problems. Driving real innovation. 𝗪𝗵𝗮𝘁’𝘀 𝗰𝗼𝗼𝗹𝗲𝗿? The Agent Catalog + Agent Card now speak one language - ORD-compliant. Standardized. Scalable. Enterprise-ready. And yes - Mohawk Industries is already on it. Real use case. Real results. 𝗔2𝗔 isn't just a system. It's a comprehensive architecture that enables seamless connectivity across platforms, empowering intelligent, scalable business solutions. 𝗛𝗲𝗿𝗲’𝘀 𝗵𝗼𝘄 𝗶𝘁 𝘄𝗼𝗿𝗸𝘀: ✅ 𝗔𝗴𝗲𝗻𝘁 𝗟𝗮𝘆𝗲𝗿 ➞ Agents play a pivotal role in ensuring smooth communication between SAP applications and other platforms. ➞ Integrated through the 𝗝𝗼𝘂𝗹𝗲 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗔𝗴𝗲𝗻𝘁 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻, enabling smarter business decisions. ➞ Each agent is trusted, secure, and connects various applications like 𝗦𝗔𝗣 𝗕𝗗𝗖, 𝗦𝗔𝗣 𝗖𝗼𝗻𝗰𝘂𝗿, 𝗦𝗔𝗣 𝗦𝘂𝗰𝗰𝗲𝘀𝘀𝗙𝗮𝗰𝘁𝗼𝗿𝘀, 𝗮𝗻𝗱 𝗦𝗔𝗣 𝗦/4𝗛𝗔𝗡𝗔. ✅ 𝗢𝗥𝗗 𝗔𝗴𝗴𝗿𝗲𝗴𝗮𝘁𝗼𝗿 & 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗼𝗿 ➞ Serves as the core for managing agent connections. ➞ Facilitates smooth, seamless integration and data flow across different cloud environments. ➞ Ensures efficient communication, integration, and orchestration with platforms like 𝗔𝗪𝗦, 𝗔𝘇𝘂𝗿𝗲, 𝗮𝗻𝗱 𝗚𝗼𝗼𝗴𝗹𝗲 𝗖𝗹𝗼𝘂𝗱. ✅ 𝗖𝗹𝗼𝘂𝗱 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 ➞ A unified platform for connecting 𝗔𝗪𝗦, 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗔𝘇𝘂𝗿𝗲, and 𝗚𝗼𝗼𝗴𝗹𝗲 𝗖𝗹𝗼𝘂𝗱 for better scalability and flexibility. ➞ A cloud-agnostic design that ensures your business isn't locked into one specific provider. ➞ Real-time connectivity ensures that data and services are always in sync. ✅ 𝗦𝗲𝗹𝗳 𝗥𝗲𝗴𝗶𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻 & 𝗧𝗿𝘂𝘀𝘁 ➞ Streamlined process for agent registration, ensuring a hassle-free experience. ➞ Built-in trust protocols to ensure that data is always secure and reliable. ✅ 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗔𝗜 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 ➞ Powered by 𝗦𝗔𝗣 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗔𝗜, enabling intelligent decision-making based on real-time data insights. ➞ Helps businesses improve processes, reduce inefficiencies, and drive smarter operations. ➞ Fully integrated with other SAP applications to enhance automation and decision-making. This architecture is more than just a solution; it's a framework built for a future of seamless interoperability. 𝗔2𝗔 ensures that businesses can scale faster, innovate smarter, and connect more securely. Embrace 𝗔2𝗔 𝗜𝗻𝘁𝗲𝗿𝗼𝗽𝗲𝗿𝗮𝗯𝗶𝗹𝗶𝘁𝘆 to unlock smarter connections, improved business efficiency, and secure integrations across systems. 🔗 P.S. Bookmark this to see how A2A can transform your enterprise. Save 💾 ➞ React 👍 ➞ Share ♻️   Follow Alok Kumar for all things related to SAP and business innovation