Breaking Down AI Communication: MCP and A2A Protocols Explained

These protocols build new foundations that let AI systems communicate better. The A2A protocol helps AIs work together through standard methods. They can find each other's abilities, share tasks, and create secure multi-agent workflows. MCP works like a "USB-C port for AI applications" that gives universal access to company data and tools. The push toward standardization has gained momentum with more than 50 major partners like Salesforce, Deloitte, and UiPath joining the effort.

This piece explains how these AI communication technologies function and their real-life applications in major companies. You'll learn about what makes them such a fundamental change in AI systems' future operations. Understanding these protocols will show you the next wave of AI communication tools that will drive tomorrow's smart systems.

The Need for Standardized AI Communication Protocols

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The rise of AI systems needs a basic change in how these technologies share information. AI models used to work alone, which created data silos and substantially limited what they could do. This setup was like computers before standard networking protocols turned standalone machines into today's connected internet.

• From isolated agents to collaborative systems
  AI systems used to work as standalone units with few outside connections. Every integration needed custom code. This created scattered ecosystems that stymied progress and growth. As one industry expert notes, "Early research in this field focused primarily on scenarios where communication protocols were explicitly designed and implemented by human developers".
  AI's path to shared work started with multi-agent reinforcement learning (MARL). Multiple AI agents share spaces and match their actions to maximize results. At first, agents traded information in set formats. Their creators could see and control these interactions. But recent advances show agents developing skills beyond their original programming.
  Tests at leading AI labs have shown agents naturally developing quick ways to signal each other when given complex shared tasks. These new communication channels mark big progress. Systems can now figure out the best ways to share information and reach group goals.
  Standard communication protocols are breaking down old barriers. AI systems can now:

  • Find each other's capabilities on their own
  • Share complex information naturally
  • Work together well on complex tasks
  This change from standalone models to connected agents marks a fundamental shift in how AI systems work and add value in real applications.
• Why traditional APIs fall short for AI-to-AI communication
  Traditional APIs were built for predictable, non-AI uses with expected behaviors. But as we move into the age of Large Language Models (LLMs) and agentic AI, these old approaches show big limits.
  "Traditional APIs are stateless by design. But LLMs often require memory persistence — e.g., to carry a conversation thread, track an agent's plan, or refine actions based on prior steps". This basic mismatch creates a major roadblock to good AI communication.
  Regular APIs expect structured input with fixed rules. LLMs work mainly through natural language. The gap between what you want (like "book a flight") and structured API rules gets more complex based on context. This creates "scattered, fragile systems that are hard to maintain, reason about, and scale".
  AI use cases don't have fixed outcomes. Unlike regular apps, AI-driven systems vary across several areas:

  1. Apps get different inputs based on user actions and context
  2. LLMs read prompts based on context, leading to varied rather than fixed behavior
  3. AI outputs trigger actions based on patterns instead of set rules
  4. Multi-agent environments create complex interaction scenarios
  Regular APIs lack the context awareness needed to handle AI communication well. This gap brings big risks around privacy violations, data security breaches, and following regulations.
  LLMs and AI agents are becoming key parts of our systems as we move toward smart, active, and shared software. Using tools built for rigid systems to connect them will limit what these systems can do together.

Understanding the Model Context Protocol (MCP)

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The Model Context Protocol (MCP) brings a fundamental change in how AI systems connect with external data sources and tools. It works as a standardized bridge between AI applications and their environments. MCP addresses the biggest problem of giving AI models structured, live access to information beyond their training data.

• Structured context injection via MCP servers
  MCP uses a client-server architecture that separates AI models from external systems cleanly. The architecture has three main components:

  • Host: The AI-powered application (like Claude Desktop or an IDE)
  • Client: Embedded within the host to handle communication with servers
  • Server: Lightweight programs that expose external capabilities to AI models
  This design lets one host connect with many servers at once. MCP servers combine smoothly with data sources of all types, both local (files, databases) and remote (APIs, cloud services). The server gets only the needed data when an AI needs information. It formats this into a context block and sends it to the model in a standard format.
  The real benefits show up when MCP servers work with existing APIs or services to get live data. To cite an instance, developers have built MCP servers for Git, GitHub, Postgres, Slack, and filesystem access. These tools are now accessible to compatible AI systems.
• Tool invocation and function routing in MCP
  Tools are essential building blocks in the MCP specification. They let servers provide executable functions to clients. AI models can perform calculations, work with external systems, and take real-life actions through these tools.
  Each tool has a consistent structure:
  {
     name: string,                // Unique identifier
     description?: string,    // Human-readable explanation
     inputSchema: {          // JSON Schema for parameters
        type: "object",
        properties: { ... }
     },
     annotations?: {         // Optional behavior hints
        readOnlyHint?: boolean,
        destructiveHint?: boolean,
        idempotentHint?: boolean
     }
  }
  Developers create McpServerToolType classes and implement McpServerTool methods that connect to external services to add functions. The protocol manages discovery. Clients can list available tools through the tools/list endpoint and use them via the tools/call endpoint.
  This standardization removes the need for custom integrations between each AI model and external tool. MCP changes what was an "M×N problem" (connecting M models to N data sources) into a simpler "M+N" scenario. Each component needs to implement the protocol only once.
• Live data access through standardized endpoints
  MCP stands out by giving AI models live data access through consistent endpoints. It enables instant data retrieval and processing instead of using cached or snapshot information.
  The protocol supports multiple transport methods for this access:

  • Stdio (Standard Input/Output): Used mainly for local processes and development
  • HTTP + SSE (Server-Sent Events): Perfect for networked services and remote integrations
  All MCP communication uses JSON-RPC 2.0 as the message standard. This gives consistent structure across all interactions. The protocol stays uniform whether an AI retrieves Git commits, queries a database, or processes sensor data.
  Two-way communication in the protocol allows flexible integrations. It supports streaming context from server to AI model or sending status updates from client to server. This feature helps especially when you have ongoing awareness of changing conditions. AI communication tools can maintain contextual understanding across complex workflows effectively.

How the Agent-to-Agent (A2A) Protocol Enables Collaboration

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Google's Agent-to-Agent (A2A) protocol tackles a different challenge than MCP's connection of AI models with external tools and data. The protocol helps independent AI agents find each other's capabilities and work together effectively. A2A creates a standard language that lets AI systems communicate across organizational boundaries, whatever frameworks or hosting environments they use.

• Agent Cards and capability discovery
  Agent Cards are the heart of A2A. These standardized JSON files work like digital business cards for AI agents. You'll find them at /.well-known/agent.json. These machine-readable metadata files show:

  • The agent's identity and description
  • Endpoint URL to receive requests
  • Authentication requirements
  • Supported skills and capabilities
  • Protocol compatibility information
  An agent looking for help first checks the potential partner's Agent Card through this location system. This discovery system lets agents adapt without hardcoded integrations. A2A lets agents find partners with specific skills automatically, which creates an ecosystem where agents adapt to each other's changing abilities.
• Task delegation using JSON-RPC over HTTPS
  A2A uses JSON-RPC 2.0 over HTTPS as its main way to communicate. This system uses common web technologies, which makes it easy to integrate on any platform. The protocol organizes everything around a "task" lifecycle:

  5. Client agent creates a unique Task ID and starts a request
  6. Remote agent processes the request and sends back responses
  7. Task reaches its final state (completed/failed/canceled)
  Messages contain "parts" - complete content pieces with specific types like text, images, or data. This standard format helps agents know exactly what information they get. The system creates consistency between different systems while protecting each agent's independence and private information.
• Stateless communication and streaming updates
  A2A supports two ways to communicate:
  The tasks/send method gives quick back-and-forth exchanges. For more complex tasks that need ongoing updates, A2A provides up-to-the-minute streaming through Server-Sent Events (SSE) with the tasks/sendSubscribe method.
  This streaming feature lets agents keep connections open and get instant progress updates ("Analyzing document...", "Generating draft...") and partial results. A2A also has a reliable push notification system that works even when the client agent goes offline.
  A2A's adaptability helps developers build modular agent ecosystems. AI communication flows naturally between independent systems, which breaks down the usual barriers between isolated AI implementations.

MCP and A2A in Real-World Multi-Agent Workflows

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MCP and A2A protocols are changing how enterprise workflows operate by creating systems where AI agents work together naturally. These protocols work hand in hand - MCP links agents to tools and knowledge sources, while A2A lets agents talk directly to each other.

• Customer service automation with inventory and logistics agents
  These protocols show their true value in complex customer support scenarios. A customer asks about their order status. The customer service agent checks with an inventory agent about product availability and talks to a logistics agent about shipping times. The agent can also work with a finance agent to process refunds without human help. Agents can cooperate even when they run on different models or platforms.
  AI agents make customer service better in supply chains. They handle routine jobs like tracking orders and answering questions, which makes customers happier. IBM Research points out, "Either one should be able to initiate a conversation or delegate a task... within an organization, you might have an agent triaging customer queries that should be able to send their customer to the right service agent which can then close out the ticket".
• Live operations management using sensor and video data
  MCP and A2A shine in situations that need live data processing. Theme park AI agents watch video streams and work with operations agents to move staff around based on crowd sizes. The protocols help connect data sources like video feeds, sensors, and ticketing systems with the agents that process this data.
  Manufacturing AI keeps things running smoothly. Supply-chain analytics AI tells robotic assembly unit agents about predicted delays, which helps adjust production schedules on the fly. Agents share data through standard endpoints, so systems can exchange information reliably.
• Enterprise examples: Salesforce, SAP, Zoom, Box
  Big companies are quickly adopting these protocols. Salesforce and SAP show how A2A can bridge separate systems. Their integration keeps customer data, quotes, and orders in sync across cloud and local systems. Reports show that "Sales now have daily updated customer data as well as competitor and company information for tailor-made offers and campaigns".
  Zoom uses A2A to help agents interact on its open platform, which makes cooperation better. Box and Auth0 show how companies can standardize agent authentication with consistent identity flows. This boosts security while keeping systems compatible.
  The Salesforce ecosystem lets AI agents use MCP to access customer relationship data along with external support and billing information. This helps them give quick, accurate answers to customer questions. Business users find this helpful when they need current information about sales performance and customer engagement across different platforms.

Why MCP and A2A Are Foundational for the Future of AI Systems

Image Source: Logto blog

The rise of standardized AI communication protocols marks a radical alteration in the way intelligent systems will evolve and merge into the digital world. MCP and A2A are the foundations of technologies that will alter the map of AI communication by addressing core architectural challenges.

• Decoupling intelligence from integration
  "Headless AI agents" showcase one of the biggest advantages these protocols offer - the separation of intelligence from implementation details. Organizations can develop their intelligence layer once and deploy it everywhere by decoupling AI capabilities from specific interfaces. The same AI operates consistently whether it runs through a customer-facing chatbot, a backend workflow, or a mobile application.
  This separation creates central control points for governance and compliance that reduce development complexity. Teams can implement updates at the core rather than across multiple implementations when business rules change or AI models improve. All touchpoints benefit from these changes at once.
• Modular agent ecosystems across vendors
  These protocols revolutionize by creating interoperable, multi-vendor AI ecosystems. MCP and A2A help organizations build modular systems where teams can update or replace components without disrupting the entire architecture. This approach reduces the risk of technological lock-in and helps teams adapt to emerging capabilities quickly.
  The economic impact is substantial. Organizations can focus their investments on specific components that provide the most value for their needs. This strategy helps allocate resources efficiently and potentially speeds up returns on AI investments.
  More than 50 major tech companies (including Atlassian, Box, Langchain, and PayPal) contribute to these protocols. The industry shows unprecedented momentum toward standardization.
• Comparison to the rise of REST and JSON in the 2010s
  MCP and A2A's adoption path matches how REST APIs and JSON revolutionized web development in the 2010s. Web services relied on fragmented, proprietary interfaces that limited interoperability before standardization. REST and JSON created a common language that realized new capabilities and efficiencies.
  These AI protocols build the infrastructure for systems that exceed the sum of their parts—just as HTTP and related standards enabled the modern web. Companies that make use of these communication frameworks will deploy sophisticated capabilities more efficiently than competitors who rely on less integrated approaches.