In the rapidly evolving world of AI, we’re witnessing the birth of a new kind of internet—an ecosystem not of websites and users, but of intelligent agents. These autonomous systems increasingly need to collaborate, share data, and execute tasks. And just like the early internet needed standards like HTTP and TCP/IP, the AI agent era demands its own protocols.
Two of the biggest contenders are MCP (Multimodal Communication Protocol) by Anthropic, and A2A (Agent-to-Agent Communication) by Google DeepMind. Both aim to standardize how AI agents operate—but from different angles.
Here’s what you need to know.
1. What They Are
- MCP is a framework to help AI agents access files, APIs, databases, and tools. Think of it as a clean, structured way to plug models into the real world. It provides a standard format to expose capabilities to an agent.
- A2A is designed for agent-to-agent communication. It lets one agent ask another to do something, manage responses, and agree on output formats. It’s about coordination between minds, not just tools.
2. Why They Both Exist
The future of AI isn’t one big model doing everything—it’s many specialized agents working together across companies, platforms, and clouds.
- A2A makes this multi-agent collaboration possible, especially when the agents come from different vendors or frameworks.
- But agents still need to interact with the world—databases, spreadsheets, cloud tools, PDFs—and MCP is the layer that helps them do just that.
In short:
A2A = communication between agents
MCP = connection to data, files, and tools
3. How They Fit Together
Google puts it clearly:
“Use MCP for tools and data, A2A for agent-to-agent communication.”
In practice:
- An agent might use MCP to analyze a document or pull records from a CRM.
- Then use A2A to delegate part of its task to another agent—say, summarizing findings or translating content.
It’s a layered architecture:
MCP handles ground-level inputs, A2A manages collaboration and task handoff between intelligent components.
4. Overlap and Friction
As with any competing standards, there’s overlap—and competition.
- Both protocols offer discovery mechanisms:
- MCP lists available tools
- A2A defines Agent Cards, profiles that describe what each agent can do
- A2A brings more coordination and security primitives to the table, including task delegation, response verification, and better sandboxing
- MCP’s current security posture is weaker, lacking built-in authentication or agent validation
As agent networks scale, interoperability, error handling, and governance become more critical. Neither protocol is fully there yet, but A2A is pushing harder on coordination and trust.
5. Long-Term Stakes
This isn’t just a protocol war—it’s about who sets the rules for the next phase of AI infrastructure.
- Governance will be essential. Who ensures agents behave? Who audits their decisions?
- MCP may stay useful for legacy system access (databases, files), but fully agentic platforms may bypass it with direct APIs
- Expect both sides to encroach on each other’s territory—A2A might add data access features, MCP might improve authentication and delegation
- The future could be hybrid: MCP for legacy I/O, A2A for live coordination
6. What To Watch
Here’s where developers and security professionals should keep their eyes:
- Will MCP evolve to include secure authentication, sandboxing, and access control?
- Will A2A become the de facto standard for building agent systems at scale?
- Can Google make Agent Cards the universal language for agent discovery?
- Which ecosystem builds stronger developer trust with tooling, transparency, and governance?
Bottom Line
- Use MCP to plug into tools and data
- Use A2A to connect and coordinate your agents
- If current trends hold, A2A could define the foundation of how agentic systems interact and scale across organizations
The AI protocol war is just beginning. If you’re building secure, intelligent systems—or protecting the ones that do—it’s time to get familiar with both MCP and A2A.