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As AI agent technology rapidly evolves, it brings with it a wave of new protocols, SDKs, and architectural concepts. For developers, acronyms like A2A (Agent2Agent), MCP (Model Context Protocol), and ADK (Agent Development Kit) can start to blend together — making it difficult to understand what each does and how they fit into the bigger picture.
In this second article of the series (read the first article on Understanding A2A here), we’ll untangle that confusion. You’ll learn:
- What makes A2A and MCP distinct but complementary
- How they work together to enable agent collaboration and context sharing
- Where ADK fits into the overall ecosystem — and why it matters for anyone building the next generation of intelligent agents
Protocol Overload: Why It Feels Confusing — and Why That’s Okay
If you’ve been exploring the world of Agentic AI, you’ve probably come across acronyms like A2A, MCP, and ADK — often in the same sentence. At first glance, it’s easy to assume they’re competing technologies or overlapping solutions. But in reality, they solve very different problems in the agent stack.
This moment of ambiguity is common in fast-moving tech fields — especially when standards are still taking shape. Rather than seeing this as a barrier, think of it as a map just beginning to be drawn. This article is your guide through that emerging landscape: helping you understand how these technologies differ, how they work together, and where each one fits into a modern agent architecture.
A2A vs. MCP: Complementary Protocols for Smarter Agents
Two of the most talked-about protocols in the evolving agent ecosystem are Google’s Agent2Agent (A2A) and Anthropic’s Model Context Protocol (MCP). While they’re often mentioned in the same breath, they serve very different purposes — and understanding that distinction is key.
MCP focuses on how a single agent interfaces with its environment — APIs, tools, file systems, databases, and more. You can think of MCP as a kind of “universal adapter” or USB port: it standardizes how agents “plug into” external resources, allowing them to access the tools they need to think, reason, and act.
A2A, in contrast, is all about agent-to-agent communication. It defines how agents discover each other, negotiate tasks, exchange messages, and collaborate across systems. If MCP equips an agent to be powerful on its own, A2A enables that agent to become part of a networked team.
This relationship is intentionally designed to be synergistic, not competitive. As Google puts it:
A2A ❤️ MCP
That phrase captures the spirit of collaboration between these standards. There’s no protocol war here — just well-defined roles in a bigger system:
Analogy: A Workshop Full of Mechanics
Imagine a garage full of expert mechanics:
- MCP the standard that ensures each mechanic can reliably use tools — whether that’s a torque wrench, diagnostic tablet, or inventory system. It defines how a mechanic uses resources.
- A2A is the protocol that lets those mechanics talk to each other, delegate tasks, and sync efforts. “You check the brakes, I’ll work on the engine.” It defines how they collaborate.
In other words, MCP makes the agent capable, and A2A makes it collaborative.
For developers, this isn’t an either/or decision — you’ll often use both. Together, A2A and MCP lay the groundwork for powerful, autonomous, and interconnected agents that can reason, act, and work in harmony across complex systems.
Synergy in Action: How A2A and MCP Work Together in Multi-Agent Systems
The real magic of A2A and MCP emerges when they operate together inside a multi-agent architecture. Here’s how they complement each other in practice:
Step-by-Step Scenario:
- Task Received
Agent A (say, a general-purpose customer service agent) gets a complex request — for example, retrieving billing history for a customer. - Delegation Decision
Agent A realizes it doesn’t have direct access to billing systems, so it decides to delegate this to Agent B — a specialized billing agent. - Agent Discovery & Request (via A2A)
Using A2A, Agent A locates Agent B through its Agent Card and sends a structured task request. This might include back-and-forth negotiation or clarification. - Tool Access (via MCP)
Agent B accepts the request and needs to access the company’s billing database and an external payment API. It uses MCP to securely connect to these tools — retrieving the necessary data and executing any required actions. - Response Formulation
With the billing data in hand, Agent B prepares a response. - Response Delivery (via A2A)
Agent B sends the result back to Agent A using A2A — as a properly formatted Artifact. - Task Completion
Agent A incorporates the response into its final output and completes the user’s original request.
This workflow shows how A2A enables agents to collaborate, while MCP equips each agent with the tools it needs to act. They’re not competing standards — they’re complementary layers in a scalable agent system:
- A2A = collaboration protocol
- MCP = capability interface
As a developer, understanding both is crucial. In a single workflow, an agent might act as:
- an A2A client, delegating tasks
- an A2A server, responding to peers
- and a MCP user, connecting to APIs and data sources
This interplay allows you to build modular, flexible agent ecosystems where responsibilities are cleanly separated — yet tightly integrated.
Feature Breakdown: Comparing A2A and MCP Side-by-Side
To highlight how A2A and MCP differ — and how they complement each other — here’s a side-by-side comparison across their core capabilities and design priorities.
One of the clearest distinctions between A2A and MCP lies in their approach to data formats and modalities.
A2A is intentionally modality-agnostic — it’s built to handle a wide variety of content types, from plain text and structured JSON to files and, in the future, rich media like audio or video. This flexibility aligns with the dynamic, often conversational nature of agent-to-agent communication, where exchanged content might evolve over time or vary based on the task.
MCP, on the other hand, focuses primarily on structured data exchange — ideal for the more rigid, contract-based interactions common in agent-to-tool communication. Think of it like a standardized API call: the agent knows exactly what to send and what to expect in return.
This design difference reflects the fundamental role each protocol plays:
- Use A2A when agents need to negotiate, collaborate, or share varied types of information
- Use MCP when an agent must interface with a tool, database, or service that expects a fixed input/output format
Why Two Standards? Embracing Layered Design Over One-Size-Fits-All
Some may wonder: why not just create one unified protocol for everything agents do?
The short answer: clarity and specialization win.
Rather than bundling tool integration and inter-agent communication into a single, bloated standard, the ecosystem has naturally evolved into a layered model — much like how the internet separates networking (TCP/IP) from application protocols (like HTTP).
- MCP focuses on capabilities — how an individual agent interfaces with tools, data, and APIs.
- A2A handles coordination — how multiple agents collaborate, delegate, and interact with each other.
This separation isn’t a limitation — it’s a strength. It reflects a maturing architecture where each layer does one thing well, making systems easier to build, maintain, and scale.
For developers, the takeaway is simple: use the right protocol for the right job — and lean into the layered design that’s quietly becoming the backbone of Agentic AI.
Introducing ADK: One Path to Building A2A-Compliant Agents
Agent Development Kit (ADK) is a toolkit designed to help developers build agents that can participate in A2A-based communication. ADK provides prebuilt components, libraries, and scaffolding to streamline the development of A2A-compatible agents.
However, it’s important to note that A2A is not tied to ADK. The protocol is an open, framework-agnostic standard — and Google has emphasized this by offering sample integrations with a variety of existing agent frameworks, including CrewAI, LangGraph, LlamaIndex, and Semantic Kernel.
This approach gives developers flexibility:
- If you’re looking for a tightly integrated toolkit, ADK is available.
- If you’re already invested in another framework, you can still adopt A2A through lightweight integration.
In essence, ADK is a reference implementation, not a requirement. The core idea is to make A2A adoption as accessible as possible, regardless of what tools or platforms a team prefers.
Conclusion: Building the Future, One Protocol at a Time
As AI agents grow more capable, the challenge isn’t just what they can do — it’s how they work together. With A2A, we get a robust framework for inter-agent collaboration. With MCP, we get a clean interface for agents to interact with tools. And with ADK, Google offers a practical path to bring A2A to life.
These aren’t competing technologies. They’re complementary building blocks in a layered, flexible agent architecture. The takeaway for developers? You don’t have to choose between them — you can, and often should, use all three to build smarter, more connected, and more maintainable agentic systems.
What’s Next?
Stay tuned — the future of agentic AI isn’t just distributed, it’s collaborative.
If you found this helpful, give it a share or drop a comment.
And Thank you for reading! Feel free to connect and reach out on LinkedIn to share feedback, questions and what you build with ADK and A2A