Agent-to-Agent Communication Protocols in AWS

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.

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.

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 announced Agent2Agent Protocol, how is it related to MCP and what is this all about ? 🤖 𝟏. 𝐌𝐨𝐝𝐞𝐥 𝐂𝐨𝐧𝐭𝐞𝐱𝐭 𝐏𝐫𝐨𝐭𝐨𝐜𝐨𝐥 (𝐌𝐂𝐏): 𝐌𝐨𝐝𝐞𝐥-𝐭𝐨-𝐓𝐨𝐨𝐥/𝐃𝐚𝐭𝐚 𝐈𝐧𝐭𝐞𝐫𝐚𝐜𝐭𝐢𝐨𝐧 𝐏𝐮𝐫𝐩𝐨𝐬𝐞: MCP is designed to be a universal standard for how an AI model (or an application housing a model, sometimes called an "agent" in this context) securely connects to and interacts with external tools, APIs, and data sources (called "MCP servers"). 𝐆𝐨𝐚𝐥: To provide the AI model with necessary "context" (like files, database entries, real-time information) from these external sources and allow the model to trigger actions (like updating a record, sending a message) using those tools. It aims to eliminate the need for custom, one-off integrations for every tool. 𝐈𝐧𝐭𝐞𝐫𝐚𝐜𝐭𝐢𝐨𝐧 𝐓𝐲𝐩𝐞: Primarily Client (AI model/app) <-> Server (Tool/API/Data Source). 𝐀𝐧𝐚𝐥𝐨𝐠𝐲: Think of MCP like a standardized USB port or HTTP protocol for AI. It allows any compatible AI model to "plug into" and use any compatible external tool or data source without needing a special adapter each time. 𝐅𝐨𝐜𝐮𝐬: Enhancing the capabilities of a single AI model/application by giving it secure and standardized access to the outside world. 𝟐. 𝐀𝐠𝐞𝐧𝐭-𝐭𝐨-𝐀𝐠𝐞𝐧𝐭 (𝐀𝟐𝐀) 𝐂𝐨𝐦𝐦𝐮𝐧𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐏𝐫𝐨𝐭𝐨𝐜𝐨𝐥𝐬: 𝐀𝐠𝐞𝐧𝐭-𝐭𝐨-𝐀𝐠𝐞𝐧𝐭 𝐈𝐧𝐭𝐞𝐫𝐚𝐜𝐭𝐢𝐨𝐧 𝐏𝐮𝐫𝐩𝐨𝐬𝐞: These protocols define standards for how multiple distinct autonomous AI agents communicate directly with each other to collaborate, coordinate tasks, negotiate, and share information.   𝐆𝐨𝐚𝐥: To enable complex multi-agent systems where agents can work together effectively, delegate tasks, and achieve goals that a single agent couldn't manage alone. This includes agents potentially built by different developers or organizations. 𝐈𝐧𝐭𝐞𝐫𝐚𝐜𝐭𝐢𝐨𝐧 𝐓𝐲𝐩𝐞: Agent <-> Agent 𝐌𝐞𝐜𝐡𝐚𝐧𝐢𝐬𝐦: Often based on established theories defining message types (inform, request, query), message structures, interaction protocols, and sometimes shared languages/ontologies. Newer protocols like Google's A2A build on web standards (HTTP, JSON-RPC) for interoperability. 𝐀𝐧𝐚𝐥𝐨𝐠𝐲: Think of A2A protocols as a shared language, grammar, and set of conversational rules (etiquette) that allow different agents to understand each other and work together cooperatively. 𝐅𝐨𝐜𝐮𝐬: Enabling communication, collaboration, and coordination between multiple distinct AI agents. MCP Official: https://lnkd.in/gRMcrwpn A2A Official: https://lnkd.in/g6PCJZWn Follow Arpit Adlakha for more!

🧠 [Primer] Model Context Protocol (MCP) + Agent2Agent Protocol (A2A) • http://mcp.aman.ai - MCP is an open, AI-native standard that enables seamless integration between LLMs and external tools or data sources. Think of it as a “USB-C for AI,” allowing agents to discover, connect to, and interact with everything from APIs to local files -- without hardcoding specific integrations.  - A2A is a protocol for inter-agent communication that enables autonomous AI agents to coordinate tasks, share capabilities, and collaborate dynamically. It handles discovery, task delegation, messaging, and even privacy-aware content routing -- allowing agents to act as specialized collaborators in complex workflows. 🔹 Model Context Protocol (MCP) Protocol   • Why MCP?      • General Architecture      • How MCP Works      • MCP vs. API      • Comparative Analysis      • When to Use MCP vs. Traditional APIs      • Security, Updates, and Authentication      • Getting Started with MCP: High-Level Steps      • Use-Cases of MCP in Real-World Development Scenarios      • MCP Servers List        - Awesome MCP Servers        - Model Context Protocol Servers        - Composio MCP Servers        - Smithery      - Zapier's Managed MCP Server 🔹 Agent2Agent (A2A) Protocol      • A2A Design Principles      • How A2A Works      • Core Protocol Mechanics        - Capability Discovery        - Task Management        - Collaboration and Messaging        - User Experience (UX) Negotiation        - Content Routing and Privacy      • Real-World Scenario: Candidate Sourcing      • Implementation Architecture      • Integration and Future Roadmap Primer written in collaboration with Vinija Jain. #artificialintelligence #llms #agents