How A2a Improves AI Collaboration

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.

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 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.

Google recently announced their new Agent2Agent (A2A) protocol with more than 50 partners, including Writer. But what is it and why does it matter, especially for enterprise developers? AI is rapidly moving toward agent-based systems that can handle complex tasks, but these systems often operate in isolation. A2A is an open standard that allows different AI agents to communicate and collaborate while maintaining their independent operation. With A2A, agents can exchange context, status, instructions, and data without sharing their internal operations, maintaining the proprietary nature of each agent while allowing them to work together. What makes A2A particularly valuable is its enterprise-ready approach with key principles: 1. Opaque execution: agents don't share their internal thoughts or tools 2. Async-first design: built for long-running tasks and human-in-the-loop processes 3. Modality-agnostic: supports text, audio/video, forms, and other interaction types 4. Simple implementation: leverages existing standards like HTTP and JSON-RPC The protocol centers around task completion where agents communicate through well-defined objects: - Tasks: stateful entities tracking progress and exchanging messages - Artifacts: results generated by agents that can be streamed or updated - Messages: context, instructions, or other communication between agents - Parts: individual content pieces with specific types and metadata As with everything in this field, A2A is still evolving. Google is actively seeking community and partner feedback to refine the specification. If you're building agent-based systems, this is definitely worth exploring. Blog: https://lnkd.in/gSN6YkYv Repo: github.com/google/A2A Docs: https://lnkd.in/g66WYcWt Enterprise readiness: https://lnkd.in/gFU8q_37

𝗔𝟮𝗔 (𝗔𝗴𝗲𝗻𝘁-𝘁𝗼-𝗔𝗴𝗲𝗻𝘁) 𝗮𝗻𝗱 𝗠𝗖𝗣 (𝗠𝗼𝗱𝗲𝗹 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗣𝗿𝗼𝘁𝗼𝗰𝗼𝗹) are two emerging protocols designed to facilitate advanced AI agent systems, but they serve distinct roles and are often used together in modern agentic architectures. 𝗛𝗼𝘄 𝗧𝗵𝗲𝘆 𝗪𝗼𝗿𝗸 𝗧𝗼𝗴𝗲𝘁𝗵𝗲𝗿 Rather than being competitors, 𝗔𝟮𝗔 𝗮𝗻𝗱 𝗠𝗖𝗣 𝗮𝗿𝗲 𝗰𝗼𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝗿𝘆 𝗽𝗿𝗼𝘁𝗼𝗰𝗼𝗹𝘀 that address different layers of the agent ecosystem: • 𝗔𝟮𝗔 is about agents collaborating, delegating tasks, and sharing results across a distributed network. For example, an orchestrating agent might delegate subtasks to specialized agents (analytics, HR, finance) via A2A25. • 𝗠𝗖𝗣 is about giving an agent (often an LLM) structured access to external tools and data. Within an agent, MCP is used to invoke functions, fetch documents, or perform computations as needed.    𝗧𝘆𝗽𝗶𝗰𝗮𝗹 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄 𝗘𝘅𝗮𝗺𝗽𝗹𝗲: • A user submits a complex request. • The orchestrating agent uses 𝗔𝟮𝗔 to delegate subtasks to other agents. • One of those agents uses 𝗠𝗖𝗣 internally to access tools or data. • Results are returned via A2A, enabling end-to-end collaboration25.    𝗗𝗶𝘀𝘁𝗶𝗻𝗰𝘁 𝗦𝘁𝗿𝗲𝗻𝗴𝘁𝗵𝘀 • 𝗔𝟮𝗔 𝗲𝘅𝗰𝗲𝗹𝘀 𝗮𝘁:   Multi-agent collaboration and orchestration   Handling complex, multi-domain workflows   Allowing independent scaling and updating of agents   Supporting long-running, asynchronous tasks54 • 𝗠𝗖𝗣 𝗲𝘅𝗰𝗲𝗹𝘀 𝗮𝘁:   Structured tool and data integration for LLMs   Standardizing access to diverse resources   Transparent, auditable execution steps   Single-agent scenarios needing a precise tool    𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗮𝗹 𝗔𝗻𝗮𝗹𝗼𝗴𝘆 • 𝗠𝗖𝗣 is like a 𝘶𝘯𝘪𝘷𝘦𝘳𝘴𝘢𝘭 𝘤𝘰𝘯𝘯𝘦𝘤𝘵𝘰𝘳 (USB-C port) between an agent and its tools/data. • 𝗔𝟮𝗔 is like a 𝘯𝘦𝘵𝘸𝘰𝘳𝘬 𝘤𝘢𝘣𝘭𝘦 connecting multiple agents, enabling them to form a collaborative team.    𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 𝗮𝗻𝗱 𝗖𝗼𝗺𝗽𝗹𝗲𝘅𝗶𝘁𝘆 𝗖𝗼𝗻𝘀𝗶𝗱𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀 • 𝗔𝟮𝗔 introduces many endpoints and requires robust authentication and authorization (OAuth2.0, API keys). • 𝗠𝗖𝗣 needs careful sandboxing of tool calls to prevent prompt injection or tool poisoning. Both are built with enterprise security in mind.    𝗜𝗻𝗱𝘂𝘀𝘁𝗿𝘆 𝗔𝗱𝗼𝗽𝘁𝗶𝗼𝗻 • 𝗔𝟮𝗔: Google, Salesforce, SAP, LangChain, Atlassian, Cohere, and others are building A2A-enabled agents. • 𝗠𝗖𝗣: Anthropic (Claude Desktop), Zed, Cursor AI, and tool-based LLM UIs.   Modern agentic systems often combine both: 𝗔𝟮𝗔 𝗳𝗼𝗿 𝗶𝗻𝘁𝗲𝗿-𝗮𝗴𝗲𝗻𝘁 𝗼𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻, 𝗠𝗖𝗣 𝗳𝗼𝗿 𝗶𝗻𝘁𝗿𝗮-𝗮𝗴𝗲𝗻𝘁 𝘁𝗼𝗼𝗹 𝗶𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻. This layered approach supports scalable, composable, and secure AI applications.

I'm really intrigued by Google's new Agent-to-Agent (A2A) protocol. When Google announced A2A, I wondered if we were witnessing the start of a protocol war with Anthropic's MCP. Both seemed to be tackling AI system integration. They’re both open protocols, but they’re solving different problems. MCP is about giving LLMs structured access to tools, APIs, and external context. It's like that scene in The Matrix where Neo downloads kung fu directly into his brain. Through the protocol, the model gains a solid understanding of a tool's capabilities and interfaces, allowing it to execute commands more reliably and precisely. A2A is about letting autonomous agents talk to each other directly. They can discover each other's capabilities, negotiate task assignments, and coordinate complex workflows across different systems. It's like giving models walkie-talkies and saying, "You figure it out." You might expect A2A would just absorb what MCP does, but Google took a different approach. They designed A2A to operate at a higher layer of abstraction, creating a layered architecture where MCP handles the vertical integration (model-to-tool) and A2A manages the horizontal collaboration (agent-to-agent). Together, they form a complete stack. I love the idea that these two protocols are complementary. To watch the building blocks of interoperable AI come together feels like a special moment in history. Every protocol decision today shapes how AI systems will talk to each other tomorrow.

Why Google A2A Changes the Infrastructure Game The introduction of A2A isn’t just a feature — it’s a structural shift in how we design, deploy, and scale intelligent systems. Here’s how the landscape transforms: 1. From Monolithic Agents to Modular Meshes Today’s GPTs are powerhouses — but they’re also monoliths. A2A enables agent specialization: • One agent might own context and memory. • Another could handle financial calculations. • A third might act as a compliance gatekeeper. These agents coordinate, not compete — like microservices for intelligence. 2. Cross-Vendor Agent Ecosystems An enterprise could run GPT-4 for planning, Gemini for scheduling, and Claude for summarizing — all talking via A2A. No vendor lock-in. No fragile API chaining. Just agents working together, as peers. 3. Agent App Stores Become Reality Imagine a future where you “install” a legal review agent, connect it via A2A, and it instantly collaborates with your internal project agents. This is the equivalent of the App Store moment — but for interoperable, intelligent services. 4. Decentralized Intelligence In this model, no single agent needs to “know it all.” Intelligence is distributed, resilient, and adaptive — a mesh of task-specific minds that share goals, not architecture. 5. Composable Enterprise Workflows A2A allows companies to compose workflows across agents the same way we compose software today. Think BPMN for agents — but driven by intelligence, not rigid logic.

𝗛𝗼𝘄 𝗱𝗼 𝘄𝗲 𝗺𝗮𝗸𝗲 𝘀𝘂𝗿𝗲 𝘁𝗵𝗮𝘁 𝗺𝗶𝗹𝗹𝗶𝗼𝗻𝘀 𝗼𝗳 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀 𝗰𝗮𝗻 𝘁𝗮𝗹𝗸 𝘁𝗼 𝗲𝗮𝗰𝗵 𝗼𝘁𝗵𝗲𝗿 — 𝘀𝗲𝗰𝘂𝗿𝗲𝗹𝘆, 𝘀𝗰𝗮𝗹𝗮𝗯𝗹𝘆 𝗮𝗻𝗱 𝘃𝗲𝗻𝗱𝗼𝗿-𝗻𝗲𝘂𝘁𝗿𝗮𝗹? Multi-agent networks and agent-to-agent communication are set to become some of the most important topics in AI over the next few years. A2A - the Agent-to-Agent Protocol from Google, launched yesterday, could be an important building block for the future. It could be the missing layer that finally makes multi-agent AI work at scale. It’s open-source by default and already backed by 50+ players — including Salesforce, LangChain, and SAP. 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗔2𝗔? A2A is an open standard that lets AI agents: - communicate   - coordinate   - and complete tasks together — across orgs, tech stacks and frameworks. So it works basically like: → One agent sends a task   → Another agent completes it   → No brittle integrations. No vendor lock-in. No proprietary walls. 𝗛𝗼𝘄𝗲𝘃𝗲𝗿, 𝘁𝗵𝗲𝗿𝗲’𝘀 𝗮𝗹𝗿𝗲𝗮𝗱𝘆 𝗮 𝗺𝗮𝗷𝗼𝗿 𝗺𝗶𝘀𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 — 𝗔2𝗔 ≠ 𝗠𝗖𝗣. A2A is not a replacement for MCP. It complements it. MCP (Model Context Protocol) connects agents to tools, APIs and enterprise systems. A2A connects agents to each other across organizational and technical boundaries Think of it like this: → A2A = agents talking to agents → MCP = agents accessing tools and resources Both are designed to work together as part of a broader, interoperable agent architecture. (see diagram below.) --- Multi-agent networks and agent-to-agent communication will become increasingly important topics in AI over the next few years. And the Google release is a strong signal that we now have an open standard designed specifically for this. Most agent-based systems today are constrained by brittle integrations and closed ecosystems. A2A introduces a shared language for agents to collaborate — in a way that can actually scale. But for couse, it’s early and only time will tell if it becomes the standard. But for now, this is a meaningful step forward. Here’s the full announcement and more info: https://lnkd.in/dkCxu-kb

Today, Google introduced its new Agent2Agent protocol —a key step toward a future of AI Agent interoperability— and Box is announcing support for it. No single tool has all the data a user or business needs for most workflows, so we need Agents to be able to talk to each other. Salesforce will have AI Agents that understand the inner workings of CRM, Workday will have AI Agents that understand HR workflows, Box has AI Agents that understand content and documents, and so on. It’s easy to conceive of a world where we have tens of thousands of these tool Agents and then billions or trillions of customized Agents that are extensions of those. So openness becomes key. Workflows need data from multiple systems to complete the task — like a sales report that needs documents and CRM data or an HR task that needs HR policies and employee details — and that’s where A2A comes in. This protocol gives AI Agent providers a way of talking to each other and simplifying the way that Agents communicate, understand each other’s capabilities, and so on. It’s great to see more interoperability, along with MCP, emerging in the AI space right now, as this will define the future of most IT stacks.