Google launched A2A in April 2025. Anthropic launched MCP in November 2024. By December 2025, both protocols sat under the Linux Foundation's Agentic AI Foundation — co-governed by OpenAI, Google, Microsoft, Anthropic, AWS, and Block. By February 2026, MCP had crossed 97 million monthly SDK downloads.
These protocols aren't competing. They solve different problems at different layers. But because both involve AI agents and both involve communication, the question "A2A vs MCP — which should I use?" is everywhere. It's the wrong question. By the end of this post you'll understand why, and you'll know exactly how to use both.
We build AI automation systems for businesses across India, the UAE, and Singapore — from WhatsApp agents that save clients 130+ hours per month to agentic content operations that run our own publishing stack. We use MCP in production daily. Here's our unfiltered take on where these protocols sit and what they mean for what we build.
The One-Sentence Summary
MCP connects an AI agent to external tools and data (vertical integration — agent to resources).
A2A connects AI agents to other AI agents (horizontal integration — agent to agent).
That's it. Everything else follows from this distinction.
MCP: The Tool Layer
We've covered MCP in depth in What Is MCP and Why It's the HTTP of the Agentic Web → and built a server from scratch in How to Build an MCP Server →. Here's the 60-second version for context.
MCP standardises how an AI model connects to tools, databases, APIs, and data sources. It uses a client-server architecture over JSON-RPC. The AI is the client. The tool (your API, your database, your Shopify store) is the server. The AI discovers what tools are available, decides which to call, and MCP handles the communication.
Think of it as the AI's hands. Without MCP, every AI-to-tool integration was custom — the M×N problem, where M models times N tools equals M×N one-off integrations. MCP collapses that to M+N.
The analogy that resonates most from our production work: MCP is like having a standardised connector between your AI brain and every tool in your workshop. Before it existed, every tool had a different plug. Now they all fit the same port.
A2A: The Agent Communication Layer
Google launched A2A at Google Cloud Next 2025 in April, explicitly positioning it as a complement to MCP, not a replacement. In the announcement, Google stated: A2A addresses challenges they identified deploying large-scale, multi-agent systems — specifically the problem of agents built by different vendors on different frameworks being unable to communicate.
Where MCP is about an agent accessing tools, A2A is about agents accessing other agents. The conceptual model:
- Your orchestrator agent receives a user request
- It breaks the task into subtasks and delegates to specialist agents
- Those specialist agents might be: a data analysis agent, a content generation agent, a booking agent, a compliance checking agent
- A2A provides the standard for orchestrators and specialists to discover each other, communicate securely, and hand off tasks
A2A uses HTTP/JSON transport with Agent Cards — structured metadata documents that describe an agent's identity, capabilities, supported task types, and authentication requirements. Agents discover each other by fetching Agent Cards, then communicate through a defined message format with explicit task lifecycles (submitted → working → completed/failed).
Security in A2A is built into the spec: OAuth 2.0, PKCE, and API keys for authentication. Task lifecycles are tracked with explicit states. This makes A2A designed for enterprise environments where you need audit trails, compliance, and cross-organisational trust.
The Linux Foundation incorporated A2A in December 2025 (IBM's Agent Communication Protocol merged into A2A in August 2025, consolidating the landscape). A2A v1.0 stable release shipped in Q1 2026. By February 2026, over 100 enterprises had signed on as AAIF supporters.
The Correct Mental Model: Layers, Not Competitors
The most useful way to think about this is a protocol stack:
┌──────────────────────────────┐
│ A2A: Agent ↔ Agent │ Orchestrators delegating
│ (Google + Linux Foundation) │ to specialist agents
├──────────────────────────────┤
│ MCP: Agent ↔ Tools │ Agents calling databases,
│ (Anthropic + Linux Foundation) │ APIs, and services
└──────────────────────────────┘
A concrete example of how they work together: A D2C brand's AI system might have an orchestrator agent that handles customer service requests. Via A2A, it delegates to specialist agents — an order management agent, a product recommendation agent, a returns processing agent. Each specialist agent, in turn, uses MCP to connect to the actual tools it needs — the order database, the product catalogue API, the ERP system.
A2A governs the conversation between agents. MCP governs each agent's conversation with its tools. Remove either layer and the system breaks.
Google put it well in the A2A documentation, using a car repair shop analogy: MCP is the protocol connecting specialist agents to their structured tools (raise the platform, turn the wrench). A2A is the protocol enabling the customer to work with the shop employees, describe the problem, receive updates, and get results.
Technical Architecture: How They Differ Under the Hood
MCP architecture:
- Client-server over JSON-RPC 2.0
- Persistent bidirectional connection
- Transport options: STDIO (local) or Streamable HTTP (remote)
- Primitives: Tools (callable functions), Resources (readable data), Prompts (templates)
- Capability negotiation on connect
A2A architecture:
- Client-remote over HTTP/JSON (stateless per message)
- Agent Cards for discovery (JSON documents at
/.well-known/agent.json) - Message format: parts + artifacts for rich data exchange
- Task lifecycle: submitted → working → input-required → completed/failed
- Authentication: OAuth 2.0 + PKCE baked into spec
- Supports long-running async tasks with webhook callbacks
The statefulness difference matters for production systems. MCP connections are persistent — the client maintains a live session with each tool server. A2A is more HTTP-native — requests are stateless, and long-running tasks use explicit status polling or webhooks. This makes A2A more natural for cross-organisational communication where persistent sessions are impractical.
What This Means for Businesses in India and the GCC
Most of the coverage of A2A and MCP is written for US-based enterprise IT leaders or developer-focused audiences. Let me translate it for the businesses we work with.
Right now (2026), MCP is what you need. If you're a D2C brand, SaaS company, or e-commerce business looking to add AI automation — connecting AI to your Shopify store, CRM, support ticket system, or internal databases — MCP is the layer you build first. It's mature, widely adopted, has a massive ecosystem of pre-built servers, and directly enables the AI use cases that drive business ROI.
A2A becomes relevant when you're running multiple specialised agents. If you have (or are building toward) an inventory agent, a customer service agent, a pricing optimisation agent, and a logistics agent — all needing to coordinate — A2A is the protocol that makes that coordination structured, secure, and standards-based rather than custom code.
For most businesses reading this, A2A is 12–18 months out from being production-relevant. MCP is production-relevant today.
The practical recommendation: start with MCP. Build your AI-to-tool integrations on the standard. When your agent complexity grows to the point where you have multiple agents that need to delegate to each other, A2A will be there, mature, and ready.
The Governance Story: Why Neither Protocol Is Vendor Lock-In
This is important for enterprise evaluation.
Both MCP and A2A are now under the Linux Foundation's Agentic AI Foundation — co-founded by OpenAI, Anthropic, Google, Microsoft, AWS, and Block. Neither Google nor Anthropic solely controls the specification. Changes go through open RFC processes. The governance structure mirrors how HTTP and Linux are governed — truly open infrastructure, not open-washing.
This matters because it means building on either protocol is building on infrastructure that the major players are all committed to maintaining. The risk of one company pivoting and deprecating the standard — which killed many previous AI integration standards — is structurally limited.
For enterprises in regulated industries (BFSI in India, financial services in DIFC Dubai), this governance structure also provides a clearer compliance posture than proprietary vendor APIs.
Our Take: A Verdict
Every comparison post needs to take a stance, so here's ours.
A2A is better-designed than MCP for agent-to-agent communication. The explicit task lifecycle, the Agent Card discovery mechanism, and the baked-in OAuth 2.0 authentication reflect hard-won lessons from deploying multi-agent systems at Google's scale. If you're building enterprise multi-agent systems today, A2A's architecture is cleaner than rolling your own agent communication protocol.
MCP is more mature and has a larger ecosystem. 6,400+ servers, 97 million monthly SDK downloads, and native support in every major AI client means there's less friction adopting MCP than A2A right now. The tooling — MCP Inspector, FastMCP, the official SDKs — is better developed.
The businesses most likely to see immediate ROI from these protocols are those that adopt MCP for tool integration now and treat A2A as the natural next layer as their agent architectures mature.
The question was never "A2A vs MCP." It's always been "A2A and MCP, in order."
If you're thinking about what AI automation architecture makes sense for your business — whether you're starting with a single AI workflow or planning a multi-agent system — book a discovery call →. We'll give you an honest assessment of where you actually are and what to build next.
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Founder & CEO
Rishabh Sethia is the founder and CEO of Innovatrix Infotech, a Kolkata-based digital engineering agency. He leads a team that delivers web development, mobile apps, Shopify stores, and AI automation for startups and SMBs across India and beyond.
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