Common Agent Communication Protocols Explained

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.

𝗠𝗖𝗣 𝘃𝘀 𝗔2𝗔: 𝗛𝗼𝘄 𝗔𝗜 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 𝗖𝗼𝗻𝗻𝗲𝗰𝘁, 𝗦𝗶𝗺𝗽𝗹𝘆 𝗘𝘅𝗽𝗹𝗮𝗶𝗻𝗲𝗱 Wonder how AI assistants like Claude actually do things in the real world? Two emerging protocols make this possible: 𝗠𝗼𝗱𝗲𝗹 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗣𝗿𝗼𝘁𝗼𝗰𝗼𝗹 (𝗠𝗖𝗣) and 𝗔𝗴𝗲𝗻𝘁-𝘁𝗼-𝗔𝗴𝗲𝗻𝘁 (𝗔2𝗔). 𝗧𝗵𝗲 𝗕𝗮𝘀𝗶𝗰 𝗗𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝗰𝗲 MCP: Connects AI models to tools and data sources through standardized clients A2A: Connects AI agents to other AI agents 𝗧𝗵𝗲 𝗥𝗲𝘀𝘁𝗮𝘂𝗿𝗮𝗻𝘁 𝗔𝗻𝗮𝗹𝗼𝗴𝘆 MCP: The Kitchen Equipment Model Context Protocol (MCP) is like standardized kitchen equipment: • Each chef (AI) can use any stove, oven, or refrigerator without special training • The restaurant has a standard way to order ingredients from suppliers Without MCP, each chef would need custom training for every piece of equipment. 𝗔2𝗔: 𝗧𝗵𝗲 𝗖𝗵𝗲𝗳 𝗧𝗲𝗮𝗺 𝗖𝗼𝗺𝗺𝘂𝗻𝗶𝗰𝗮𝘁𝗶𝗼𝗻 Agent-to-Agent (A2A) is like how the chefs communicate with each other: • The head chef can delegate tasks to pastry chefs, sous chefs, etc. • Chefs can coordinate complex dishes that require multiple specialists Without A2A, each chef would work in isolation, unable to coordinate complex meals. 𝗥𝗲𝗮𝗹-𝗪𝗼𝗿𝗹𝗱 𝗘𝘅𝗮𝗺𝗽𝗹𝗲𝘀 𝗪𝗵𝗮𝘁 𝗠𝗖𝗣 𝗗𝗼𝗲𝘀: • Allows Claude to search your company database • Enables Katonic's ACE Co-pilot to access enterprise tools • Lets an AI assistant access your Google Calendar • Connects Claude Desktop with your local files MCP creates a standard USB-like port that connects AI to tools and data. 𝗪𝗵𝗮𝘁 𝗔2𝗔 𝗗𝗼𝗲𝘀: • Allows a research AI agent to ask a specialist AI for help • Enables a planning AI to coordinate with execution AIs • Lets multiple AI agents collaborate on a complex task A2A creates a language for AIs to communicate with each other. 𝗪𝗵𝘆 𝗧𝗵𝗶𝘀 𝗠𝗮𝘁𝘁𝗲𝗿𝘀 The future will involve teams of specialized AI agents working together: • MCP gives AI access to real-world data and tools • A2A lets multiple AIs coordinate their efforts Current State (April 2025) • MCP: Widely adopted with clients like Claude Desktop, Tempo, Windsurf, and Cursor; enterprise platforms like Katonic AI also implement MCP • A2A: Very new, just beginning to emerge as a standard Katonic has integrated MCP across their AI platform, allowing their ACE Co-pilot (which functions as an MCP client) to connect with hundreds of third-party services through a standardized interface. 𝗧𝗵𝗲 𝗕𝗼𝘁𝘁𝗼𝗺 𝗟𝗶𝗻𝗲 Think of MCP as giving AI access to tools, and A2A as giving AI the ability to work in teams. Both are essential for the future AI ecosystem.

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.

API vs MCP vs A2A — What’s the Difference, and Why It Matters There’s a lot of buzz around new AI frameworks like MCP and A2A, and many people are asking — how are these different from traditional APIs? Let’s break it down with simple examples: 1. APIs (Application Programming Interfaces) APIs are how different apps talk to each other — especially when they’re owned by different teams or companies. Example: Your app wants to check the credit score of a user. You hit an external credit bureau’s API, which returns a score. The bureau decides what data to expose, how often, and in what format. APIs are great for external world integration — clean, secure, versioned. But they require agreement, documentation, and often strict access control. You don’t see what’s behind the curtain — you just use what’s offered. 2. MCP (Model-Context Protocol) Introduced by Anthropic, MCP allows LLMs to interact with external tools and data via a standardized interface. It’s still a one-model world, but you feed it structured access to plugins and databases. Example: An LLM helping a customer support agent might use MCP to look up order history, check return status, or send a refund — all through tools connected via this common protocol. MCP is about making a single AI model smarter by giving it controlled access to context and tools. It reduces the need to hard-code every integration. 3. A2A (Agent-to-Agent Framework) This is where things shift from integration to collaboration. With A2A, different AI agents can talk to each other, negotiate, and complete tasks together — even across systems — without needing formal APIs exposed. Example: A lightweight agent on your app checks user inputs It passes the data to a document-verifying agent That agent flags inconsistencies and triggers a risk-check agent All this happens internally, without each system exposing APIs or standardizing requests It’s like RPA — but no humans in the loop, and far more intelligence. The Big Shift API: You tell me what you expose, and I’ll write code to use it. MCP: I’m one smart AI, and I know how to use tools if you give me handles. A2A: I’m part of a team of smart agents. We’ll talk, figure it out, and get the job done — no need to call IT to expose a new API. This opens up exciting new possibilities: -Teams don’t have to standardize every backend operation into an API -On-device agents can handle more on the edge -Internal workflows become more autonomous and scalable The AI architecture of the future won’t be a single genius model — it’ll be a team of capable agents talking to each other. Let me know what you think — or if you’ve started playing with these ideas. Would love to hear more use cases.

Everyone's arguing A2A vs MCP they're missing the point entirely... Most teams think they need to pick one protocol for their AI agents. That's not how this works. Reality 1: A2A handles agent collaboration. Think conference room where agents negotiate and coordinate complex workflows → not just single tasks. Reality 2: MCP connects agents to tools. Your agent needs database access? API calls? → That's MCP's workshop model in action. Reality 3: Enterprise security isn't equal. A2A ships with OAuth-level authentication built-in. MCP → needs additional configuration for secure remote access. The real difference: A2A (Google's Agent-to-Agent): → Agents operate independently, share selectively → Long-running, complex workflows → Built-in enterprise authentication → Discovery through "Agent Cards" MCP (Model Context Protocol): → Client-server architecture → Precise tool/resource access → Structured JSON schemas → Single-shot functions excel here Smart teams aren't choosing—they're combining. A2A orchestrates your agent swarm → MCP gives them tools to actually work. The truth: You need both protocols to build production-grade AI agents. One without the other → like having either steering or wheels. Choose both → Ship faster.

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.

MCP vs. A2A: Understanding Modern AI Communication Protocols 📌 Key Architectural Differences: MCP: Client-server architecture with centralized resource management A2A: Direct peer-to-peer communication between AI agents 📌 MCP Benefits: Structured access to various data sources (local and web-based) Centralized governance and security controls Specialized servers for different functional needs Better resource management for enterprise environments 📌 A2A Advantages: Secure agent collaboration without intermediaries Dynamic task and state management Streamlined UX negotiation between agents Direct capability discovery 📌 Real-world Applications: MCP excels in enterprise settings requiring oversight and governance A2A shines in scenarios needing real-time, dynamic collaboration Hybrid approaches emerging for complex systems 📌 Implementation Considerations: Scalability: MCP requires scaling server infrastructure, while A2A distributes processing load Security: MCP offers centralized security policies, A2A requires peer-level security protocols Latency: Direct A2A communication potentially reduces response times Complexity: MCP simplifies agent design but creates server dependencies 📌 Industry Trends: Large tech companies favor MCP for controlled AI deployment Research environments often implement A2A for experimental flexibility Financial services adopt MCP for regulatory compliance and audit trails Healthcare exploring both models depending on use case sensitivity As AI systems evolve from single-agent to multi-agent architectures, these communication protocols will become fundamental infrastructure considerations. The choice between MCP and A2A (or hybrid approaches) will significantly impact system flexibility, maintainability, and security posture. What's your take on these approaches? Do you see hybrid models winning in the enterprise space? Have you implemented either protocol in your organization's AI systems?

This is one of the most complete paper on Agent Protocols I've seen. Agent protocols define how AI agents communicate and interact with each other and with external tools. They’re essential for scaling Agents' systems, yet the space has been fragmented and hard to navigate. The paper introduces a two-dimensional framework for understanding any protocol: 1️⃣ Object Orientation: ▪️ Context-Oriented: How agents interact with external tools, data, and services ▪️ Inter-Agent: How agents communicate and collaborate with each other 2️⃣ Application Scenario ▪️ General-Purpose: Designed for broad, flexible applicability across many tasks ▪️ Domain-Specific: Optimized for specific use cases and environments The paper provides deep dives into popular and emerging protocols like MCP, A2A, etc. It then evaluates each protocol across four dimensions: 🔒 Security & Reliability – Are interactions safe, stable, and trustworthy? ➕ Extensibility – How easily can you plug in new tools, agents, or capabilities? ⚡ Efficiency & Scalability – Can it handle real-world workloads without breaking? 🔁 Operability & Interoperability – Is it easy to manage and integrate with other systems? If you’re building intelligent agents, this one’s absolutely worth the read.

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

Breaking News: Google Releases Agent 2 Agent Protocol! Reading a headline like this a few days ago, many of us thought: 'Well, MCP didn't last long ...' But in fact, they do different things and complement one another 👉 Here's how it works: Anthropic's Model Context Protocol (#MCP) provides an open standard for AI models to connect to data sources Google's Agent 2 Agent (#A2A) provides an open standard for AI agents to communicate MCP is like a spec for USB or HTTP - something we all need to agree on so we can get to work A2A is more like a Slack Canvas or Zoom Workspace - a secure place to assemble and focus on our project MCP seems clearly to be designed to provide a standard way for agents to access databases. The problem it's solving is the current requirement for manual point to point configurations for each agent setup to each data source - whether internal or external. As Anthropic said in its launch document, MCP is supposed to be like USB-C - that is a standard format that everyone (or a lot of people) use to make life easier to access and exchange information. Google's A2A on the other hand is more ambitious; it's also longer, more technical and more difficult to understand ... As I understand, A2A is a set of standards that will allow different agents to work together on shared tasks - even if these agents are (say) built by different companies on different stacks with different data sources Using A2A, agents can work by themselves without sharing their individual setups or data with other agents So what does A2A add? It's a way for them to communicate with one another securely over the web - and understand one another But rather than providing a strict set of requirements for inputs, outputs, formats, etc. - which MCP does, by design - A2A lets agents 'talk' to one another and figure these out depending on the task at hand A2A envisions a world where agents work together over periods of time with multiple back-and-forths and changes of direction MCP is really just a way for an agent to ping a database somewhere and get some specific information Given widespread and growing adoption for both standards by Salesforce and many others, it's clear the era of agentic collaboration is just beginning. MCP and A2A should make it easier.