Importance of Agent-To-Agent Communication

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.

Another week, another protocol. This weekend I'll be reading Google's Agent-to-Agent (A2A) protocol docs. It's fascinating how they've created a standard for AI agents to talk to each other across different platforms and vendors. My initial thought is that it's way more well-thought-out than MCP. Not here to replace it, though. Here's what caught my attention: 🔄 A2A enables true agent collaboration without requiring agents to be mere "tools" for each other. They can work together while remaining "black boxes" - preserving IP and maintaining modularity. This is a significant step beyond simple API integrations, allowing agents from different vendors to coordinate complex workflows without revealing their internal logic. 🛠️ The architecture is refreshingly practical - built on existing web standards (HTTP, JSON-RPC, SSE) that most enterprise systems already support. No exotic new protocols to implement! An A2A interaction is essentially just an HTTPS call with a JSON payload, making it straightforward to integrate with existing infrastructure. 🔐 Security is baked in from the ground up, not bolted on as an afterthought. Each agent advertises its auth requirements through "Agent Cards" (metadata documents), supporting enterprise-grade authentication and authorization. This modular approach means companies can enforce policy and trust boundaries without changing the core protocol. ⏱️ I'm particularly impressed with how A2A handles long-running tasks and asynchronous interactions. Real business processes often span minutes or hours, and A2A supports this through a subscription model and callbacks. An orchestrator agent can fire off parallel tasks to various agents and get real-time status updates without constant polling. 📊 The protocol is modality-agnostic, allowing agents to exchange not just text but structured data, files, images, and potentially audio/video. Messages are composed of "Parts" that can contain different content types, and agents can negotiate the best format for information exchange on the fly. 🧩 A2A complements Anthropic's Model Context Protocol (MCP) rather than competing with it. MCP governs how agents connect to tools/data (vertical integration), while A2A handles how agents communicate with each other (horizontal collaboration). The strategic implications here are significant. A2A could be the foundation for the "agentic enterprise," where AI capabilities become modular building blocks that can be reconfigured to meet changing business needs. It reduces integration costs and vendor lock-in that have historically hampered enterprise automation. What do you think about A2A? Is it yet another hype?

Your AI Agents Are Speaking Different Languages (And It's Costing You) 🤝 Here's a problem many teams are hitting: AI agents that excel individually but can't collaborate. Organizations have agents for customer service, data analysis, report writing, and scheduling. Each one performs well in isolation, but getting them to work together requires manual coordination between systems. Google's Agent-to-Agent (A2A) Protocol addresses this by providing a standardized communication layer for AI agents. Instead of custom integrations between every system, A2A lets agents built on different frameworks communicate directly. The practical benefit is clear - specialized agents can coordinate on complex workflows without rebuilding everything as one massive system. A LangGraph agent can work with a CrewAI agent seamlessly. This modular approach means faster development cycles, better quality outcomes, and flexibility to use best-of-breed solutions from different vendors. The protocol has backing from major tech companies and represents a shift toward collaborative agent ecosystems rather than isolated AI systems. A very comprehensive tutorial about this can be found in this repo, where people can learn to build production level agents: https://lnkd.in/dkjGZGiw ♻️ Share to let others in your network enjoy it too!

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. 🪠 ✨

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

If you’re curious about AI agents and keep hearing terms like MCP and A2A but feel overwhelmed by the jargon, here’s a quick guide to get you started: 🔹 MCP (Multimodal Communication Protocol) Gives AI agents access to external tools, APIs, and environments. It’s the toolbelt that allows them to solve complex tasks. Without MCP, agents would be stuck working in isolation. 🔹 A2A (Agent-to-Agent Communication) Enables direct dialogue between AI agents. They can delegate tasks, negotiate timelines, ask clarifying questions, and work together like a real team. Why it matters: Today’s AI agents mostly operate in isolation, each confined within its own ecosystem. They are still solo players, fragile and prone to errors when working alone.  A2A and MCP are the industry’s first steps in standardizing how AI agents collaborate. Together, they create connected, resilient systems where agents can problem-solve and accomplish more than they could individually. 👉 These are just some of the terms that will help you better understand MultiAgent Systems and where they are headed next. For more details refer to the full guide.

Why Agent-to-Agent (A2A) Collaboration Is the Future of AI—And How Agent Creators Make It Real Most AI agents today are just task-runners—efficient, but isolated. The real leap forward isn’t just automation; it’s coordination. Agent-to-Agent (A2A) protocols are setting a new industry standard, enabling AI agents to communicate, share context, and solve complex problems together. Google’s A2A protocol, for example, now has support from over 50 major tech companies, making interoperable, cross-platform agent collaboration a reality. This shift is crucial: by 2034, the global multi-agent system market is projected to reach $184.8 billion, growing at 45% CAGR as industries move toward decentralized, collaborative AI. Why does this matter? Orchestration over isolation: Multi-agent systems (MAS) enable agents to coordinate, not just automate, leading to faster, more resilient outcomes. Seamless interoperability: A2A protocols provide a universal language for agents, breaking down silos between platforms and vendors. Enterprise transformation: Businesses adopting agentic orchestration report improved efficiency and scalable AI solutions. Impact: Teams using specialized AI agents outperform single models on complex tasks. At Kroolo, our agent creator leverages modular memory/tool pipelines to build agent teams that fetch, draft, verify, and iterate—without human micromanagement. The result? Not just task completion, but true agentic orchestration. The future of intelligent automation isn’t a single AI. It’s a team—talking, collaborating, and delivering together. #A2A #AgenticAI #AIOrchestration #Kroolo #FutureOfWork