How Protocols Improve Agent Collaboration

Perhaps the most critical enabler for scalable agentic systems today is the emergence of formal agent communication protocols. As organizations start deploying multiple agent systems across sales, legal, ops, and internal tools , they’re quickly realizing that even great agents break down when they can’t talk to each other. What’s missing is not more LLMs, but standards for how agents coordinate. Let’s say your CEO gets excited by a Salesforce demo and signs up for AgentForce, a platform that promises automated contract review. The results fall short. It routes documents but lacks reasoning, memory, or recovery paths. So your engineering team layers in LangGraph to build a smarter pipeline: clause extraction, redline generation, fallback logic, and human-in-the-loop escalation. Then the CEO meets with Google, sees a demo of Agentspace, and kicks off a new MVP giving employees a Chrome-based AI assistant that can answer questions, summarize docs, and suggest revisions. Now you have three agent systems running… and none of them are compatible. This is where agent protocols become essential. They’re not frameworks or tools. They’re the glue that defines how agents interact across platforms, vendors, and use cases. There are four key types: • 𝗠𝗖𝗣 (𝗠𝗼𝗱𝗲𝗹 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗣𝗿𝗼𝘁𝗼𝗰𝗼𝗹) handles how a single agent uses tools in its environment. Whether in LangGraph or AgentForce, every tool (e.g., clause scorer, template filler) can be invoked using a standard wrapper. • 𝗔𝟮𝗔 (𝗔𝗴𝗲𝗻𝘁-𝘁𝗼-𝗔𝗴𝗲𝗻𝘁 𝗣𝗿𝗼𝘁𝗼𝗰𝗼𝗹) defines how agents exchange structured messages. A risk-analysis agent in LangGraph can send its findings to a negotiation agent in Agentspace, even if they were built by different teams. • 𝗔𝗡𝗣 (𝗔𝗴𝗲𝗻𝘁 𝗡𝗲𝘁𝘄𝗼𝗿𝗸 𝗣𝗿𝗼𝘁𝗼𝗰𝗼𝗹) ensures that agents formally declare inputs and outputs. If the finance agent in AgentForce expects a JSON summary, ANP ensures that other agents deliver it in the right format with validation. • 𝗔𝗴𝗼𝗿𝗮 𝗣𝗿𝗼𝘁𝗼𝗰𝗼𝗹 supports natural language-based negotiation between agents. When structure breaks down agents can dynamically agree on how to share context and interpret intent. The point is, these protocols enable composability. They make it possible to build agent systems where different vendors, models, and workflows can interoperate. Without them, you end up with silos—each agent powerful on its own but useless together. Most companies don’t realize they’ve hit this wall until it’s too late. They start with one agent platform, then bolt on a second, then hit scaling issues, redundant logic, or conflicting behaviors. Protocols like A2A, ANP, and Agora give you a way to standardize communication and preserve flexibility. If your org is working with multiple agent platforms or planning to integrate them across domains, it may be time to design around protocols and not just prompts.

🚨 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

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?

𝐖𝐡𝐲 𝐀𝐠𝐞𝐧𝐭-𝐭𝐨-𝐀𝐠𝐞𝐧𝐭 (𝐀𝟐𝐀) 𝐏𝐫𝐨𝐭𝐨𝐜𝐨𝐥𝐬 𝐌𝐚𝐭𝐭𝐞𝐫 𝐟𝐨𝐫 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬𝐞𝐬 AI agents are becoming more advanced. But most still operate in isolation 𝐬𝐢𝐧𝐠𝐥𝐞-𝐩𝐥𝐚𝐲𝐞𝐫 𝐦𝐨𝐝𝐞. To solve this, Google introduced 𝐀𝐠𝐞𝐧𝐭-𝐭𝐨-𝐀𝐠𝐞𝐧𝐭 (𝐀𝟐𝐀) protocols, allowing agents to communicate, collaborate, and coordinate tasks without human involvement. This shift unlocks a new level of automation, where multiple agents can handle complex workflows across business functions. A2A Protocols unlock autonomous coordination. Think of it as giving your agents Slack, APIs, and workflow engines all in one. Let’s break it down: 𝟏. 𝐃𝐞𝐜𝐞𝐧𝐭𝐫𝐚𝐥𝐢𝐳𝐞𝐝 𝐂𝐨𝐥𝐥𝐚𝐛𝐨𝐫𝐚𝐭𝐢𝐨𝐧: You’ve got agents running like AI-driven service delivery, automated onboarding, and workflow. Now, they can talk to each other in real time. - 𝑹𝒆𝒔𝒖𝒍𝒕: End-to-end workflows without human stitching. -𝑰𝒎𝒑𝒂𝒄𝒕: Faster service, synchronized operations, more automation. 𝟐. 𝐒𝐜𝐚𝐥𝐚𝐛𝐥𝐞 𝐀𝐮𝐭𝐨𝐧𝐨𝐦𝐲: One agent doesn’t need to do everything. It can delegate, coordinate, and align with others. - 𝑹𝒆𝒔𝒖𝒍𝒕: Teams of agents that adapt on the fly. - 𝑰𝒎𝒑𝒂𝒄𝒕: Broad coverage with fewer resources. 𝟑. 𝐑𝐞𝐬𝐢𝐥𝐢𝐞𝐧𝐜𝐞 𝐛𝐲 𝐃𝐞𝐬𝐢𝐠𝐧: Agents can reroute tasks, retry failed steps, escalate when needed. - Result: Systems that flex under pressure. - Impact: Fewer breakdowns, better uptime, smarter fallbacks. Here’s the signal in the noise: Smarter agents are good but 𝐬𝐦𝐚𝐫𝐭𝐞𝐫 𝐞𝐜𝐨𝐬𝐲𝐬𝐭𝐞𝐦𝐬 𝐚𝐫𝐞 𝐛𝐞𝐭𝐭𝐞𝐫. A2A helps us move from isolated AI to smart, connected automation. If you're building agents, stop thinking about what each one can do. Start thinking about what they can do together. #AgentEcosystems #A2A #AIAgents #LLM #AutonomousSystems #WorkflowAutomation #TechLeadership #AgentArchitecture #PremNatarajan

I think 2025 will be the year of multi-agent 🤖🤝🤖 Google's new Agent-to-Agent (A2A) protocol tackles a critical challenge in agent systems: enabling multiple AI agents to work as a team. The framework enables agents to communicate, assign tasks, and synchronize information with each other. If MCP is the USB protocol of agent and peripheral devices, A2A is the HTTP protocol for agents to collaborate with each other, like a service mesh. The A2A protocol carries several key features: 🌍 Capability Discovery: Just like microservices need service discovery, so does an agent network. With A2A, agents can "show their capabilities" through JSON-formatted "Agent Cards" so that client agents can select the best remote agent to complete a task. 🛠️ Structured Task Lifecycle: Tasks are treated as entities with defined states such as pending, running, completed, or failed. This structure allows for clear tracking and management of tasks throughout their execution. 🛜 Asynchronous Communication: Agents can handle long-running operations by communicating asynchronously, ensuring that tasks can progress without requiring constant real-time interaction. 🧑🤝🧑 Collaboration and Error Handling: The protocol supports collaborative task execution, where agents can seek clarification, request additional information, or handle errors through specialized recovery agents, enhancing resilience in task management. To see how this works in action, let's imagine implementing robot recruiters with multi-agents: 1️⃣ The recruitment manager can instruct the agent to search for candidates based on job descriptions, locations, skills, etc. 2️⃣ The agent collaborates with other specialized recruitment agents, integrating with platforms like LinkedIn or internal HR systems, to summarize candidate suggestions using A2A. 3️⃣ After the manager reviews the suggestions, the agent can arrange interviews or engage another agent to conduct background checks. Early prototypes of agents like Devin (AI software engineer) and Manus (general AI agent) are just a start, what's gonna really unlock the potential of agents is vast adoption of tool-using (MCP) and cross-agent collaboration (A2A).