The rise of AI is the fastest-moving technological revolution in recent history, potentially as revolutions go, in mankinds entire living history. As we move deeper into 2025, AI is rapidly evolving from standalone applications into sophisticated ecosystems of intelligent agents working together across platforms.
At the forefront of this revolution are two groundbreaking protocols: Anthropic's Model Context Protocol (MCP) and Google's Agent-to-Agent (A2A) Protocol. Together, they're creating the foundation for a new generation of interconnected AI systems that promise to transform how businesses operate.
This article explores both protocols, how they complement each other, and their potential impact on go-to-market strategies for B2B technology companies.
Anthropic's Model Context Protocol, launched in late 2024, addresses a fundamental limitation in current AI systems: their isolation from the data they need to function effectively. Even the most sophisticated AI models struggle when trapped behind information silos and legacy systems.
MCP is an open standard that enables developers to build secure, two-way connections between AI assistants and the systems where data lives – including content repositories, business tools, and development environments. It provides a universal interface for connecting AI systems with data sources, replacing fragmented integrations with a single protocol.
MCP follows a client-server architecture:
MCP Servers expose data through a standardised interface
MCP Clients (AI applications) connect to these servers to access the data they need
Server Primitives: Prompts (instructions/templates), Resources (structured data), and Tools (executable functions)
Client Primitives: Roots (filesystem access) and Sampling (LLM generations)
MCP solves what experts call the "MxN problem" – the combinatorial difficulty of integrating multiple different LLMs with numerous tools and data sources. Instead of building custom connectors for each combination, developers can standardise on MCP, significantly reducing integration complexity.
While MCP focuses on connecting AI systems to data, Google's recently announced A2A protocol tackles a different challenge: enabling autonomous AI agents to communicate with each other.
A2A is an open protocol that provides a standard way for AI agents to collaborate regardless of their underlying framework or vendor1 It enables agents to discover each other's capabilities, communicate directly, securely exchange information, and coordinate actions across platforms or applications.
A2A includes several critical components designed to enable seamless collaboration:
Agent Cards: JSON-based metadata describing an agent's capabilities, authentication requirements, and endpoints
Task Management: A comprehensive lifecycle system tracking states like 'submitted', 'working', and 'completed'
Security-First Design: Enterprise-grade authentication, encryption, and rate limiting
Multiple Communication Methods: Support for request/response patterns, server-sent events for short-running tasks, and push notifications for long-running processes
Google has enlisted over 50 technology partners to contribute to A2A's development, including major players like Salesforce, Atlassian, Box, Cohere, Intuit, LangChain, MongoDB, PayPal, SAP, ServiceNow, and Workday, along with leading service providers such as Accenture, BCG, Deloitte, and others.
Despite being developed by different companies, MCP and A2A are designed to be complementary rather than competitive. Each protocol solves a different part of the AI interoperability puzzle.
MCP and A2A address different layers of AI functionality:
MCP focuses on vertical integration – connecting AI systems to data sources and tools
A2A enables horizontal integration – allowing AI agents to collaborate with each other
In simpler terms:
MCP is about arming an agent with the knowledge it needs
A2A is about enabling agents to use their knowledge and skills together
Think of these protocols working together in layers:
MCP feeds agents with contextually relevant information from various sources
A2A enables these informed agents to collaborate effectively as a team
This synergy creates a powerful combination. Without MCP, agents collaborating via A2A might work with outdated or incomplete information. Without A2A, agents with perfect context from MCP might be unable to delegate tasks or combine their specialised skills effectively.
For B2B technology leaders, the combination of MCP and A2A opens exciting possibilities for automation and enhanced customer experiences. Here are some practical applications already emerging:
Consider a complex hiring process. Using A2A, a hiring manager can task an agent to find candidates matching specific criteria. This agent then interacts with specialised agents to source candidates, schedule interviews, and conduct background checks – all while using MCP to access relevant data from HR systems, job boards, and internal databases.
A Commerce Agent detects when a customer adds a product to cart
Using MCP, a Context Agent retrieves customer behaviour data and preferences
A Product Agent generates recommendations based on collaborative filtering and historical bundles
An Offer Agent calculates optimal discounts or bundle pricing
A UI Agent personalises how offers are presented
Through A2A, these agents coordinate seamlessly, while MCP ensures they all work from current, relevant data.
In manufacturing and logistics:
MCP connects to ERP systems, inventory databases, and logistics platforms
A2A enables coordination between demand forecasting agents, inventory management agents, and logistics planning agents
The combined system can autonomously optimize inventory levels, delivery routes, and production schedules
With MCP providing access to CRM data, market intelligence, and customer behaviour, and A2A enabling collaboration between specialised sales agents, companies can implement more sophisticated, automated lead generation and qualification processes.
Customer service can be dramatically improved when multiple specialised agents collaborate:
MCP gives agents access to complete customer history and product information
A2A enables seamless handoffs between different department agents (support, sales, technical)
Customers receive faster, more personalised service without having to repeat information
Product teams can leverage:
MCP to collect and analyse customer feedback, usage data, and market trends
A2A to coordinate between research, design, engineering, and marketing agents
The result: faster iterations and better alignment with market needs
For B2B technology providers, supporting these protocols represents a strategic advantage:
Products that implement MCP servers become easily accessible to any AI assistant
Applications that support A2A can become part of larger agent ecosystems
Interoperability reduces integration costs for customers and expands market potential
For B2B technology leaders looking to leverage these protocols:
Explore the MCP specification and SDKs released by Anthropic
Install pre-built MCP servers through Claude Desktop for testing
Begin building MCP servers for your critical internal data sources
Early adopters like Block, Apollo, Zed, Replit, Codeium, and Sourcegraph have already integrated MCP into their systems, enabling their AI to better retrieve relevant information and produce more nuanced outputs.
Review the full A2A specification draft released by Google
Explore available code samples to understand the protocol structure
Identify potential agent-to-agent workflows in your organization1Google has announced plans to release a production-ready version of the A2A protocol later this year, with growing ecosystem support1
As these protocols mature, we can expect several developments:
Future versions of both protocols will likely include:
Enhanced security features including zero-knowledge proofs for privacy
Support for federated learning and distributed transactions
Energy-efficient consensus algorithms for multi-agent systems
Experts predict potential protocol hybridization by 2026, with industry bodies developing common reference architectures to ensure seamless interoperability between MCP, A2A, and other emerging standards.
B2B companies that embrace these protocols early will gain significant advantages:
Reduced development costs through standardised integrations
Faster time-to-market for AI-enhanced products and services
Greater ability to participate in the broader AI ecosystem
Here are real-world implementations where companies are combining MCP and A2A protocols:
MCP Integration: Connects to HRIS systems, employee databases, and payroll platforms
A2A Coordination: Links recruitment, onboarding, and benefits agents
Workflow:
Recruitment agent (A2A) identifies candidate
Uses MCP to verify internal equity data from Workday
Onboarding agent (A2A) triggers IT provisioning via MCP-connected ServiceNow
Benefits agent (A2A) personalizes offers using MCP-linked healthcare plans
Impact: Reduced onboarding time from 14 days to 48 hours
MCP Implementation: Links transaction data, fraud detection models, and banking APIs
A2A Ecosystem: Connects payment processing, customer support, and risk management agents
Use Case:
A2A fraud agent detects suspicious pattern
Uses MCP to pull real-time transaction history
Collaborates via A2A with customer service agent to verify activity
Updates risk models through MCP-connected analytics tools
MCP Servers: Expose infrastructure-as-code templates and security protocols
A2A Agents: Coordinate deployment, monitoring, and cost optimization
Implementation:
Deployment agent (A2A) uses MCP to access Terraform configurations
Monitoring agent (A2A) pulls real-time metrics via MCP
Cost agent negotiates resource allocation through A2A task delegation
Outcome: 40% reduction in cloud provisioning errors
MCP Context: Inventory databases, IoT sensors, and ERP systems
A2A Network: Demand forecasting, logistics, and vendor management agents
Flow:
Forecasting agent (A2A) uses MCP to analyze sales trends
Logistics agent (A2A) books shipments via MCP-connected APIs
Vendor agent reorders stock through A2A-mediated negotiations
Benefit: 28% improvement in inventory turnover
MCP Usage: Accesses search index, user preferences, and calendar data
A2A Orchestration: Coordinates email, docs, and meeting agents
Scenario:
Meeting agent (A2A) uses MCP to prep relevant Drive files
Email agent shares minutes via A2A task handoff
Docs agent pulls research through MCP-connected Scholar API
Early adopters report that combining these protocols reduces integration costs by 57% compared to custom solutions, while increasing workflow automation scope by 3.2x. As these implementations mature, we expect more companies to adopt this dual-protocol approach for complex AI ecosystems.
Below are the most common questions business and technical leaders ask about Anthropic's Model Context Protocol (MCP) and Google's Agent2Agent (A2A) protocol, along with concise, practical answers.
MCP is best used when an AI agent needs to access external tools, databases, or data sources.
A2A is designed for scenarios where multiple autonomous agents must collaborate, delegate tasks, or exchange information.
In many enterprise applications, both protocols are used together: MCP for resource/tool access, A2A for agent-to-agent communication and orchestration.
No. MCP and A2A are complementary. MCP handles vertical integration—connecting agents to tools and data—while A2A handles horizontal integration—connecting agents to each other for multi-agent workflows.
MCP is an open standard that lets AI applications (like chatbots, IDE assistants, or custom agents) securely and consistently connect to external tools, APIs, and data sources, using a universal client-server architecture and JSON-RPC messaging.
Unlike proprietary plugins or ad hoc API function calls, MCP provides a standardised, open protocol for connecting any AI model to any tool or data source, with features like capability negotiation, secure transport, and structured context management.
Deploy or integrate an MCP server for each external tool or data source.
Embed an MCP client in your AI agent or application.
SDKs are available in Python, TypeScript, Java, and other languages.
Supports API keys, OAuth 2.1, and PKCE for authentication.
Enforces user consent and access controls for sensitive data and tool execution.
Requires explicit approval for LLM sampling or tool invocation.
Connecting AI assistants to internal business systems (CRMs, ERPs, databases).
Enhancing coding agents with live repository and ticket context.
Automating document processing by linking LLMs to file systems or APIs.
A2A is an open standard for secure, structured communication between independent AI agents—regardless of vendor or framework—enabling them to discover each other, exchange tasks, and coordinate complex workflows.
Each agent exposes an Agent Card (a JSON metadata file) describing its capabilities and endpoint.
Agents implement HTTP-based task endpoints, exchanging requests and streaming updates using JSON-RPC and Server-Sent Events (SSE).
OAuth 2.0, API keys, mutual TLS, and role-based access control.
Encrypted data exchanges and rate limiting to prevent abuse.
Multi-agent enterprise workflows (e.g., HR onboarding, supply chain).
Cross-vendor agent orchestration in customer support or finance.
Delegating subtasks to specialised agents in complex business processes.
A typical workflow:
A user submits a complex request.
An orchestrating agent uses A2A to delegate subtasks to specialised agents.
Each specialised agent may use MCP to access tools, databases, or external APIs as needed.
Results are returned via A2A, enabling modular, scalable, and secure agent ecosystems.
OpenAI plugins are proprietary and limited to specific platforms. MCP generalises the plugin concept for any AI platform, while A2A enables multi-agent collaboration—capabilities that plugins alone do not offer.
Different discovery mechanisms (Agent Cards for A2A, separate for MCP).
Need for mapping layers or adapters between protocols.
Maintaining security and context across agent and tool boundaries.
| Feature/Focus | MCP | A2A |
|---|---|---|
| Main Purpose | Tool/data access for AI agents | Agent-to-agent communication |
| Architecture | Client-server (host, client, server) | Peer-to-peer (client/remote agent) |
| Security | OAuth, API keys, user consent | OAuth, API keys, mTLS, RBAC |
| Use Case | LLMs using external tools/data | Multi-agent workflows, orchestration |
| Integration | Standardises tool access | Standardises agent interoperability |
| Typical Users | Anthropic, IDEs, B2B SaaS | Google, Salesforce, SAP, LangChain |
MCP and A2A represent a pivotal shift in how AI systems operate – moving from isolated applications to collaborative ecosystems. For B2B technology leaders, these protocols offer both technical solutions to integration challenges and strategic opportunities to enhance products and services.
The real power emerges when they work together: MCP providing rich context and data access, while A2A enables seamless collaboration between specialised agents. This combination creates AI systems that are greater than the sum of their parts – able to handle complex workflows that span organisational boundaries and leverage diverse data sources.
As you develop your AI strategy, consider how these protocols might transform your products, services, and go-to-market approach. The future of enterprise AI will be built on interoperability, and MCP and A2A are laying the foundation for that future today.
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