AI Agent Communication Protocols for Data Sharing

𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀 𝗔𝗿𝗲 𝗚𝗲𝘁𝘁𝗶𝗻𝗴 𝗦𝗺𝗮𝗿𝘁𝗲𝗿 — 𝗕𝘂𝘁 𝗢𝗻𝗹𝘆 𝗜𝗳 𝗧𝗵𝗲𝘆 𝗖𝗮𝗻 𝗧𝗮𝗹𝗸 𝘁𝗼 𝗘𝗮𝗰𝗵 𝗢𝘁𝗵𝗲𝗿 As AI shifts from single-task assistants to multi-agent systems, what truly powers this transformation isn't just bigger models — it's the rise of 𝘀𝘁𝗮𝗻𝗱𝗮𝗿𝗱𝗶𝘇𝗲𝗱 𝗽𝗿𝗼𝘁𝗼𝗰𝗼𝗹𝘀. These protocols define how agents communicate, manage memory, invoke tools, and collaborate across ecosystems. To make sense of this emerging landscape, I mapped out 𝟭𝟬 𝗺𝗼𝗱𝗲𝗿𝗻 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁 𝗽𝗿𝗼𝘁𝗼𝗰𝗼𝗹𝘀 that are shaping how agents work — together. Here’s a breakdown of what’s included: • 𝗔𝗴𝗲𝗻𝘁 𝗖𝗼𝗺𝗺𝘂𝗻𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗣𝗿𝗼𝘁𝗼𝗰𝗼𝗹 (𝗜𝗕𝗠): Lifecycle and workflow standardization • 𝗔𝗴𝗲𝗻𝘁 𝗚𝗮𝘁𝗲𝘄𝗮𝘆 𝗣𝗿𝗼𝘁𝗼𝗰𝗼𝗹: Message routing between agents and external systems • 𝗔𝗴𝗲𝗻𝘁-𝘁𝗼-𝗔𝗴𝗲𝗻𝘁 𝗣𝗿𝗼𝘁𝗼𝗰𝗼𝗹 (𝗚𝗼𝗼𝗴𝗹𝗲): Structured inter-agent collaboration (Gemini & Astra) • 𝗠𝗼𝗱𝗲𝗹 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗣𝗿𝗼𝘁𝗼𝗰𝗼𝗹 (𝗔𝗻𝘁𝗵𝗿𝗼𝗽𝗶𝗰): Unified memory and tool embedding inside LLMs • 𝗧𝗼𝗼𝗹 𝗔𝗯𝘀𝘁𝗿𝗮𝗰𝘁𝗶𝗼𝗻 𝗣𝗿𝗼𝘁𝗼𝗰𝗼𝗹 (𝗟𝗮𝗻𝗴𝗖𝗵𝗮𝗶𝗻): Standard JSON for tool metadata • 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻 𝗖𝗮𝗹𝗹 𝗣𝗿𝗼𝘁𝗼𝗰𝗼𝗹 (𝗢𝗽𝗲𝗻𝗔𝗜): Schema-enforced function execution • 𝗧𝗮𝘀𝗸 𝗗𝗲𝗳𝗶𝗻𝗶𝘁𝗶𝗼𝗻 𝗙𝗼𝗿𝗺𝗮𝘁 (𝗦𝘁𝗮𝗻𝗳𝗼𝗿𝗱): Declarative task graphs and coordination • 𝗔𝗴𝗲𝗻𝘁𝗢𝗦 𝗥𝘂𝗻𝘁𝗶𝗺𝗲: Managing stateful, long-lived agents in enterprise settings • 𝗥𝗗𝗙 𝗔𝗴𝗲𝗻𝘁 (𝗦𝗲𝗺𝗮𝗻𝘁𝗶𝗰 𝗪𝗲𝗯): Linked data agent reasoning using SPARQL • 𝗢𝗽𝗲𝗻 𝗔𝗴𝗲𝗻𝘁 𝗣𝗿𝗼𝘁𝗼𝗰𝗼𝗹: A community push toward cross-framework interoperability    This space is evolving quickly. Protocols like these are quietly becoming the 𝗿𝗲𝗮𝗹 𝗶𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 behind the AI agents of tomorrow. Whether you're designing LLM workflows or deploying AI into production systems, these are the interfaces you'll be working with next. Curious which ones you've already explored — or plan to?

𝗔𝗖𝗣 𝗮𝗶𝗺𝘀 𝘁𝗼 𝗯𝗲 𝘁𝗵𝗲 "𝗛𝗧𝗧𝗣 𝗼𝗳 𝗮𝗴𝗲𝗻𝘁 𝗰𝗼𝗺𝗺𝘂𝗻𝗶𝗰𝗮𝘁𝗶𝗼𝗻,” 𝘁𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗶𝗻𝗴 𝗼𝘂𝗿 𝗰𝘂𝗿𝗿𝗲𝗻𝘁 𝗹𝗮𝗻𝗱𝘀𝗰𝗮𝗽𝗲 𝗼𝗳 𝘀𝗶𝗹𝗼𝗲𝗱 𝗮𝗴𝗲𝗻𝘁𝘀 𝗶𝗻𝘁𝗼 𝗶𝗻𝘁𝗲𝗿𝗼𝗽𝗲𝗿𝗮𝗯𝗹𝗲 𝗮𝗴𝗲𝗻𝘁𝗶𝗰 𝘀𝘆𝘀𝘁𝗲𝗺𝘀. 👇 Every AI-first company is spinning up dozens of agents across LangChain, CrewAI, AutoGen, custom stacks, you name it. Yet they can’t natively “shake hands.” Custom connectors, brittle APIs, exploding maintenance costs... sound familiar? 🤯 𝗘𝗻𝘁𝗲𝗿 𝗔𝗖𝗣 (Agent Communication Protocol), an open-source, Linux Foundation–backed standard turning isolated bots into true teammates: 1️⃣ 𝗥𝗘𝗦𝗧-𝗯𝗮𝘀𝗲𝗱, 𝗝𝗦𝗢𝗡-𝘀𝗶𝗺𝗽𝗹𝗲: plug in with curl or Postman, zero black-box magic. 2️⃣ 𝗡𝗼 𝗦𝗗𝗞 𝗹𝗼𝗰𝗸-𝗶𝗻: use the SDK if you like convenience, skip it if you don’t. 3️⃣ 𝗢𝗳𝗳𝗹𝗶𝗻𝗲 𝗱𝗶𝘀𝗰𝗼𝘃𝗲𝗿𝘆: agents carry their own metadata so you can find them even in secure, air-gapped, or scale-to-zero setups. 4️⃣ 𝗔𝘀𝘆𝗻𝗰-𝗳𝗶𝗿𝘀𝘁, 𝘀𝘆𝗻𝗰-𝗰𝗮𝗽𝗮𝗯𝗹𝗲: perfect for long-running tasks that need resilience. What ACP is not: workflow manager, deployment tool, or brain-in-a-box. It’s the shared language. Need full orchestration? That’s where BeeAI (built by IBM Research, now in the Linux Foundation) layers on top, using ACP to deploy, scale, and share agent swarms. 𝗪𝗵𝘆 𝗶𝘁 𝗶𝘀 𝗶𝗺𝗽𝗼𝗿𝘁𝗮𝗻𝘁: Skip writing n(n-1)/2 custom integrations for every pair of agents. Swap vendors without rewiring your architecture. Unlock cross-company collaboration (think manufacturing agent chatting in real time with a logistics agent to quote accurate delivery ETAs). The fragmented era of agent tooling is ending. Standards win. Ecosystems flourish. Businesses scale faster when their agents speak the same dialect. Learn more in the detailed blog from my colleagues from IBM Research here: https://lnkd.in/gTkN_NVk Repo: https://lnkd.in/gzwVAYxK Examples: https://lnkd.in/gXWxkMsZ

Agentic AI and the Model Context Protocol (MCP): Why Apache Kafka Is the Missing Link: #AgenticAI systems are starting to move from research to real enterprise use. A key enabler of this shift is the Model Context Protocol (#MCP). MCP defines a standard way for #AI agents, tools, and applications to share context and communicate effectively. It allows agents to access structured data, call external APIs, and collaborate with other systems. However, MCP alone is not enough. It needs a #DataStreaming backbone with an #EventDrivenArchitecture to provide real-time, reliable, and scalable access to the data and events that drive intelligent behavior. This is where #ApacheKafka comes in. Kafka acts as the event broker that connects all components of an agentic architecture. It continuously streams data between systems, ensuring that AI agents always work with the most recent and accurate information. MCP defines howagents communicate; Kafka enables what they communicate: contextual, time-sensitive data that reflects the real world. With Kafka as the event layer, MCP-based agents can: - Subscribe to real-time events from business systems, IoT devices, or APIs from cloud services. - Publish insights, actions, or recommendations back to the enterprise in milliseconds. - Replay historical events for learning, auditing, or debugging. - Connect to both operational and analytical systems with full decoupling and traceability. This combination eliminates brittle point-to-point spaghetti integrations. Instead, it creates a flexible, event-driven architecture where AI agents, #microservices, and applications communicate through Kafka topics, governed and secured by the data streaming platform. In simple terms, MCP provides the language for agents to collaborate, while Kafka provides the bloodstream that keeps their context fresh and alive. Together, they form the backbone of modern agentic AI architectures: modular, adaptive, and ready to scale across cloud and edge environments. If AI agents depend on context to act intelligently, how valuable can they really be without a continuous stream of fresh, trusted data flowing through Kafka?

2025 is the Year of ACP, not just MCP. IBM has introduced a new protocol for AI collaboration called Agent Communication Protocol, building upon the foundation laid by Anthropic's Model Context Protocol. ACP takes a leap forward in how AI systems work together, allowing complex multi-agent workflows that were impossible with MCP alone. Here's how ACP works: 1️⃣ Agent Orchestration ACP enables multiple AI agents to communicate seamlessly, allowing specialized agents to combine their capabilities. 2️⃣ Standardized Messaging The protocol uses structured message formats that help agents understand each other across different frameworks and languages. 3️⃣ Task Delegation Complex problems are broken down and assigned to the most capable specialized agents, then results are assembled into cohesive solutions. 4️⃣ Framework Independence ACP works with agents built in any programming language or AI framework, removing technical barriers to collaboration. 5️⃣ Dynamic Discovery Agents can discover and utilize each other's capabilities, creating flexible AI ecosystems that evolve to meet changing needs. Whether you're building complex AI workflows or connecting specialized agents, ACP elevates what's possible, enabling deeper collaboration and more powerful solutions. Here's how ACP is architecturally different from MCP: MCP: - Focuses on connecting a single AI to external data sources and tools - Creates one-to-many relationships between an AI and various resources - Uses JSON-RPC primarily for accessing information and executing actions - Designed to expand what one AI model can access and accomplish ACP: - Centers on connecting multiple AIs to each other in collaborative relationships - Creates many-to-many networks of specialized agent capabilities - Extends JSON-RPC with agent-specific communication patterns - Designed for dividing complex tasks among specialized AI team members Understanding these distinctions matters for building the right AI infrastructure. Some problems need better tools for one AI. Others need multiple AIs working together. ACP isn't just different from MCP; it's complementary: ✅ Solves problems too complex for any single AI agent ✅ Creates AI teams with specialized members handling different aspects of a task ✅ Enables more natural workflows that mirror human team collaboration The combination of MCP and ACP is essential. MCP gives individual AIs access to tools and data. ACP helps those AIs work together as teams. Together, they create AI systems that are more capable, flexible, and effective. Over to you: What complex problems could you solve with a team of specialized AI agents working together?

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.

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

At the Project Nanda: Architecting the "Internet of AI Agents" session on the topic of Consumerization of Agentic Web, I illustrated a practical scenario to show how key agentic web protocols from Anthropic's Model Context Protocol, Google's Agent2Agent Protocol, and MIT Media Lab's Project Nanda could seamlessly orchestrate real-world, E2E agent interactions. Scenario: “Order a Large Pepperoni Pizza in 15 Minutes for Under $20” Instead of searching, browsing, and transacting across multiple apps, the user simply expresses intent: “Order a large pepperoni pizza within 15 minutes under $20.” 1. Discovery: Task Delegation to a Personal Agent * User → Personal Agent: The user delegates the request to their personal AI agent, which serves as their digital proxy. * Personalization via MCP: The agent is grounded in personal data, address, preferences, wallet access by securely connecting with an #MCP servers. This means the agent’s capabilities are transparently extended based on explicit user permissions. 2. Trust & Context: Intelligent Matchmaking with Nanda * Personal Agent → Nanda Index: The agent reformulates the user’s request, adding personalized context (like delivery location, dietary preferences). * Nanda Index: Think of #Nanda as the “semantic DNS” for agents. It performs intelligent parsing and matchmaking by, searching public and private registries for available pizzeria agents within a 2-mile radius, filtering candidates that match the price, timing, and menu requirements * Back to Personal Agent: Nanda returns a ranked list of candidate pizzeria agents, those most likely to satisfy the user’s constraints. 3. Negotiation & Selection: Multi-Agent Collaboration * Personal Agent → Candidate Pizzeria Agents (via A2A): For each candidate agent the personal agent asks a set of selection questions like Do you offer pepperoni pizza? How soon can you deliver? * Interactive Negotiation: The personal agent queries and negotiates terms (menu, pricing, delivery window) with candidate agents using the #A2A protocol, which standardizes secure, transparent agent-to-agent messaging and workflows. 4. Transaction: Order Placement & Payment * Personal Agent → Selected Pizzeria Agent (via A2A): Once a pizzeria agent is selected, the personal agent. Places the order, shares the user’s delivery address and facilitates payment. * Transaction Confirmed: All this happens in the background, no forms, no manual price checks, no app switching. Why Does This Matter? This is not just a pizza-ordering story, it’s a preview of how the Agentic Web transactions will radically improve digital experiences by: * Reducing Cognitive Load on Humans * Empowering Data Ownership & Safety * Enabling Interoperability * Laying the Foundation for Trusted, Autonomous AI Collaboration As AI moves beyond chatbots and apps, the next wave is agent-based automation, where the “Internet of AI Agents” becomes the new OS for consumer tasks and enterprise workflows. #AgenticWeb

MCP vs A2A vs ANP vs ACP - Decoding AI Agent Protocols The future of AI agents isn’t just about smarter models - it’s about how they talk to each other. Communication protocols define whether agents can collaborate seamlessly or operate in silos. I put together this one-page comparison to demystify the four major approaches shaping agent communication: → MCP (Model Context Protocol) Manages and shares model context in distributed systems Ideal for AI model coordination & context-aware sharing Use Case:  • Healthcare – synchronizing diagnostic models (imaging, labs, patient records) for holistic decision-making. • Enterprise AI platforms – ensuring consistent context sharing across cloud-hosted AI services. • AI research environments – enabling reproducible experiments with context-aware knowledge transfer. → A2A (Agent-to-Agent Protocol) Direct peer-to-peer communication Great for multi-agent task execution & decentralized AI agents Use Case:  • Autonomous vehicles – exchanging traffic, hazard, and navigation data in real time. • Industrial robotics – coordinating assembly-line tasks between specialized robots. • IoT ecosystems – smart appliances negotiating energy consumption without central control. → ANP (Agent Networking Protocol) Enables network-level communication across multiple agents Supports large-scale agent networks & distributed AI ecosystems Use Case:  • Smart cities – traffic lights, weather sensors, and utility systems optimizing urban operations together. • Disaster response – drones, weather agents, and logistics systems collaborating in real time. • Telecom networks – distributed AI agents dynamically managing bandwidth and routing. → ACP (Agent Communication Protocol) Standardizes interaction rules between agents Ensures structured, schema-driven communication Use Case:  • Financial services – fraud detection, compliance, and trading agents exchanging structured, auditable messages. • E-commerce platforms – inventory, recommendation, and support agents working seamlessly across systems. • Defense & security – ensuring autonomous surveillance and monitoring agents follow strict messaging standards. → Why this matters: As AI agents become the backbone of enterprise workflows, choosing the right communication protocol will determine scalability, interoperability, and real-world impact. This infographic breaks down definitions, purposes, communication types, use cases, scalability, and technologies-so you can see which protocol best fits your AI strategy. → Which protocol do you think will dominate the future of multi-agent AI systems? →Follow Rajeshwar D. for more insights on AI #AI #AIAgents #ArtificialIntelligence #MultiAgentSystems #GenerativeAI #LLMOps #FutureOfAI #MachineLearning #AIEngineering #EnterpriseAI

𝗧𝗵𝗲 𝗺𝗼𝘀𝘁 𝗰𝗼𝗺𝗽𝗿𝗲𝗵𝗲𝗻𝘀𝗶𝘃𝗲 𝘀𝘂𝗿𝘃𝗲𝘆 𝗼𝗻 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁 𝗣𝗿𝗼𝘁𝗼𝗰𝗼𝗹𝘀 𝗷𝘂𝘀𝘁 𝗱𝗿𝗼𝗽𝗽𝗲𝗱! ⬇️ LLMs can now plan, reason, use tools, and collaborate. But most of them don’t speak the same language. And without a shared protocol, we’ll never unlock scalable, autonomous systems. It’s the missing infrastructure of the AI age. A team of researchers from Shanghai Jiao Tong University (great to see my former university here) just released what might be the most comprehensive survey on AI Agent Protocols to date. Their goal? To map the emerging landscape of how LLM-powered agents interact with tools, data, and each other — and why current fragmentation is holding us back. 𝗧𝗵𝗲 𝗽𝗮𝗽𝗲𝗿 𝗯𝗿𝗲𝗮𝗸𝘀 𝗻𝗲𝘄 𝗴𝗿𝗼𝘂𝗻𝗱 𝗯𝘆: * Proposing a new classification system for protocols * Comparing 13+ protocols (like MCP, A2A, ANP, Agora) * Outlining the technical gaps we need to solve * Showing how protocol design will shape the future of multi-agent systems and collective AI 𝗛𝗲𝗿𝗲 𝗮𝗿𝗲 6 𝗞𝗲𝘆 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆𝘀 𝘄𝗵𝗶𝗰𝗵 𝘀𝘁𝗼𝗼𝗱 𝗼𝘂𝘁 𝘁𝗼 𝗺𝗲: ⬇️ 1. 𝗔𝗴𝗲𝗻𝘁 𝗜𝗻𝘁𝗲𝗿𝗼𝗽𝗲𝗿𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗜𝘀 𝗕𝗿𝗼𝗸𝗲𝗻 ➜ Today’s agents are siloed. Everyone builds their own APIs, their own wrappers, their own formats. This is the early-internet problem all over again. 2. 𝗣𝗿𝗼𝘁𝗼𝗰𝗼𝗹𝘀 𝗔𝗿𝗲 𝘁𝗵𝗲 𝗡𝗲𝘄 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 ➜ Think TCP/IP — but for agents. These standards will determine whether tools and agents can communicate across vendors, platforms, and environments. 3. 𝗠𝗖𝗣 𝗜𝘀 𝗟𝗲𝗮𝗱𝗶𝗻𝗴 𝗳𝗼𝗿 𝗧𝗼𝗼𝗹 𝗨𝘀𝗲 ➜ Anthropic’s Model Context Protocol (MCP) is one of the most advanced protocols for agent-to-resource interactions — and it fixes key privacy issues in tool invocation. 4. 𝗔2𝗔 𝗮𝗻𝗱 𝗔𝗡𝗣 𝗘𝗻𝗮𝗯𝗹𝗲 𝗠𝘂𝗹𝘁𝗶-𝗔𝗴𝗲𝗻𝘁 𝗖𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝗼𝗻 ➜ Google’s A2A is enterprise-grade and async-first. ANP, on the other hand, is open-source and aims to create a decentralized Agent Internet. 5. 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻 𝗚𝗼𝗲𝘀 𝗕𝗲𝘆𝗼𝗻𝗱 𝗦𝗽𝗲𝗲𝗱 ➜ The report introduces 7 dimensions for assessing agent protocols — from security to operability to extensibility. It’s not just about performance. It’s about trust, adaptability, and integration. 6. 𝗨𝘀𝗲 𝗖𝗮𝘀𝗲𝘀 𝗦𝗵𝗮𝗽𝗲 𝗣𝗿𝗼𝘁𝗼𝗰𝗼𝗹𝘀 ➜ A protocol that works for a single-agent chatbot may fail in an enterprise-grade multi-agent orchestration scenario. Architecture matters. So does context. As we move toward a true Internet of Agents, the paper outlines the standards, challenges, and architectural shifts we need to unlock scalable, interoperable agent ecosystems. Important dicussion and great insights! At the end of the day, it’s about enabling agents to coordinate, negotiate, learn, and evolve — forming distributed systems greater than the sum of their parts. You can download the survey below or in the comments!

If you want to understand how AI Agents actually work together… start by understanding their protocols. AI agents don’t collaborate magically. They communicate, share memory, negotiate tasks, and stay safe because a whole ecosystem of protocols makes it possible. Teams focus on models and tools. But it’s the protocol layer that decides whether your agents scale, or fail. This map breaks down the core building blocks every agentic system relies on: 1. Core & Widely Used Protocols These are the fundamental standards that let agents talk to each other, execute tasks, and interact with tools in a structured, predictable way. They form the backbone of any agent-based architecture. 2. Transport & Messaging This layer keeps agents connected. It handles event streams, async messaging, real-time communication, and reliable delivery - everything needed for fast, fault-tolerant workflows. 3. Memory & Context Exchange Agents can’t reason or collaborate without shared context. These protocols help them store state, exchange histories, and retrieve past knowledge so the system behaves consistently over time. 4. Security & Governance Every agent interaction must be audited, authorized, and safe. These standards ensure identity, access control, compliance, and safe execution, especially when agents touch production systems. 5. Coordination & Control This is the orchestration layer. It handles oversight, delegation, decision-making, and task handoffs - enabling multi-agent pipelines to work as one coherent system. - Why this matters As AI agents move from prototypes to production, understanding these protocol layers becomes essential. Models generate intelligence - but protocols create order, safety, and scale. If you want agents that can collaborate, negotiate, and execute reliably, this is the foundation to build on.