Google A2A vs Anthropic MCP - What They Mean for the Future of Agentic AI
The evolution of AI agents is entering a new era - one where tools and models no longer operate in silos but collaborate, coordinate, and adapt in real-time. At the forefront of this shift are two open protocols from two of the most influential players in AI - Google’s Agent-to-Agent (A2A) and Anthropic’s Model Context Protocol (MCP).
Both aim to make agent-based systems modular and interoperable, but they tackle different pieces of the puzzle. One focuses on how agents talk to each other, and the other on how an agent interacts with data and tools. For product teams, AI engineers, and decision-makers looking to build the next generation of intelligent apps, understanding A2A and MCP isn’t optional - it’s foundational.
The Two Protocols in Focus
Google Agent-to-Agent (A2A)
|A2A is a communication protocol for AI agents to discover, negotiate, and coordinate with one another. It brings structured turn-based collaboration, agent capability discovery (via agent cards), and support for complex task exchanges between agents. A2A enables agents to behave more like autonomous peers - less like plugins and more like teammates.
Anthropic Model Context Protocol (MCP)
MCP, on the other hand, defines how a language model interacts with external data and tools; within the bounds of its own orchestration. It standardizes how context is fed to the model, how it can request tools, and how it responds with structured actions. Think of it as a universal adapter between LLMs and the real world.
A Layered View of Agentic Systems
The most helpful way to understand these two protocols is to see them as addressing different layers in an agentic system -
MCP works inside the agent - between the model and the tools it can call. It structures internal reasoning and action-taking.
A2A works between agents, allowing separately scoped agents to interact, delegate, and negotiate.
In practice, a planning agent using MCP might decide to delegate a subtask - like scheduling - to a remote agent via A2A. This handoff and feedback loop is what makes a composable, distributed agent system work.
Why This Matters for AI Builders
At YBM Labs, we have learnt to see these protocols not just as implementation details, but as strategic primitives. Here’s why:
Composable AI workflows
Developers can build smaller, specialized agents (e.g., finance copilot, meeting scheduler, code reviewer) that plug into each other via A2A and augment themselves via MCP.
Infrastructure standardization
Instead of reinventing the wheel, teams can rely on common formats and flows. This drastically shortens build cycles and improves reliability.
Interoperability across models and vendors
Both A2A and MCP are model-agnostic. You can mix Claude, GPT, Gemini, or local models, enabling agentic architectures that are truly modular.
Open ecosystem opportunities
Whether it’s building your own MCP server for internal data or launching an A2A-compatible agent to interoperate with client workflows, the door is open for plug-and-play innovation.
Real-World Implications
Let’s take examples from sectors where YBM Labs already operates:
Healthcare -
Imagine a clinical documentation agent using MCP to access EMRs and lab reports, and then initiating an A2A interaction with a medical billing agent to validate insurance eligibility - all in one fluid workflow.
Fintech -
A fraud detection agent might trigger an A2A-based alert to a compliance officer agent while using MCP to fetch KYC data and transaction logs. The result? Real-time, explainable decisioning.
EdTech -
A curriculum planning agent could use MCP to pull data from learning analytics tools and then use A2A to coordinate with a content-generation agent, ensuring timely and contextual delivery.
Takeaways for Teams Building Agentic AI
Use MCP when you want your agent to reason over live tools and data.
Use A2A when you want multiple agents (or org units) to collaborate autonomously.
Together, they enable decentralized AI systems - agent networks that work more like organizations than monoliths.
Just like APIs reshaped the web, A2A and MCP are reshaping AI - from isolated prompts to cooperative systems. At YBM Labs, we’re actively building for this future.
Want to explore how Agentic AI systems can fit into your workflows?
Let’s talk.
Book a call with our applied AI team - https://ybmlabs.com