A2A and MCP: Essential Protocols for AI Agent Development
MCP structures how an agent accesses its tools and data. A2A defines how multiple agents coordinate with each other. These two open protocols address distinct and complementary problems: MCP alone works for a single agent, but once your architecture becomes multi-agent, A2A is essential. This article breaks down the mechanics of each protocol, explains why they complement each other, and points to the resources you need to integrate them into your projects.
Source:
A2A vs MCP: AI Agent Communication Explained
· IBM Technology · Published March 2, 2026
· 11min
MCP has become the backbone of AI agent development. Launched by Anthropic in November 2024, it addresses the M×N integration problem that was stalling the ecosystem: instead of writing separate integrations between every LLM and every tool, MCP provides a standardized layer. You write an MCP server once for a CRM, a database, or a Git repository, and any compatible client can use it without rewriting the integration. Adoption has been rapid — thousands of open-source MCP servers available within months, native integration in the leading IDEs and frameworks. That standardization is what made agents practically usable at scale.
But MCP only covers half of the agentic problem. Once you move to architectures where multiple specialized agents need to coordinate, delegate tasks, and exchange results, MCP alone is no longer enough. That is exactly what A2A — the Agent-to-Agent protocol launched by Google in April 2025 — addresses. In 2026, MCP and A2A together form the reference foundation for any serious agentic system. Understanding both means understanding the architecture of distributed AI as it is being built right now. For teams building real agentic systems, these are not optional protocols: they are the foundations.
Why do AI agents need standardized protocols?
An AI agent, taken in isolation, can reason and generate output. What it cannot do natively is talk to your database, interact with your Git repository, or delegate a task to another agent running on a different framework. Without standardization, every connection becomes a bespoke integration. With five agents and ten tools, you could be maintaining fifty custom connectors across framework updates.
The fragmentation problem was especially acute in multi-agent architectures. A Salesforce-based agent delegating to a ServiceNow agent required a manually written bridge. A Vertex AI agent could not coordinate with an AWS Bedrock agent without a custom adapter layer. As our May 2026 press review on agentic AI covered, this fragmentation was identified as the main barrier to scaling multi-agent systems. MCP solved one layer of this problem for resources and tools; A2A completed the solution for inter-agent coordination.
A2A: how do agents discover each other and coordinate?
A2A solves horizontal coordination between agents. Its central mechanism is the agent card: a standardized JSON manifest each agent publishes to describe its capabilities, expected inputs, and outputs. Other agents discover these cards dynamically, identify available skills, and delegate tasks without prior manual configuration. As Martin Keen puts it in the IBM Technology video, it is “a resume for agents” — each agent advertises what it can do, and others decide the division of labor in real time.
The transport layer is plain HTTP with JSON-RPC 2.0 payloads. Any standard web infrastructure can host an A2A agent, immediately gaining the benefits of the existing web stack: routing, load balancing, authentication, logging. No proprietary server required. For long-running workflows, A2A supports Server-Sent Events (SSE): an agent can push progressive updates to another in near real-time while processing, without the coordinating agent having to wait for the final result before proceeding.
A2A exchanges are also modality agnostic. Agents handling different data types — text, images, structured files, JSON objects — can coordinate without manual conversion. One agent generates a design mockup, another reviews it, a third handles client approval: all within a single coordinated flow, distributed across specialized agents that may run on entirely different frameworks or be operated by different organizations.
MCP: how does an agent access its tools and data?
Where A2A handles coordination between agents, MCP handles the vertical connection between an agent and its resources. The architecture has two components: an MCP host (the application where the agent runs) and an MCP server (the service that knows how to interact with a specific resource). The agent calls the primitives the MCP server exposes — it does not need to know the implementation details of a database or a third-party API.
There are three primitives. Tools are invocable functions: searching a database, committing code, sending a Slack message. Resources are readable data: files, records, application state. Prompts are pre-built templates for recurring interactions. The key advantage: write an MCP server once for a CRM, and any compatible host application can use it — no rewriting when you switch models or host applications.
Transport depends on where the server lives: stdin/stdout for local servers (an IDE plugin accessing the local file system, for example), HTTP with streaming for remote servers. The ecosystem now includes thousands of open-source MCP servers for GitHub, Slack, relational databases, file systems — ready to integrate without writing the connector yourself. As covered in our Google I/O 2026 announcements overview, the MCP spec 2026-07-28 introduced further improvements to streaming and stateless architecture that simplify server development.
Are A2A and MCP competing or complementary?
The confusion comes from the fact that the two protocols appear to address similar problems. In practice, they operate on different layers and compose naturally. The clearest summary comes from the IBM Technology video itself: “A2A for agents talking to agents, MCP for agents talking to tools and data.” MCP is vertical (agent to resource); A2A is horizontal (agent to agent).
A concrete example makes the complementarity tangible. In a retail inventory management system: an inventory agent uses MCP to read and write a product database. When it detects a low stock level, it uses A2A to notify an internal ordering agent. That ordering agent uses A2A to communicate with external supplier agents — built by third-party companies, running on different frameworks. Each of those external agents uses MCP to access its own tools. The two protocols interlock without friction, each within its own defined scope.
How do you get started with A2A and MCP in your projects?
For MCP, the starting point is the official documentation at modelcontextprotocol.io, with SDKs in Python and TypeScript. If you already work with Claude Code or an IDE with MCP integration, servers can be configured directly in your environment. The MCP spec 2026-07-28 — one of the highlights from our Google I/O 2026 coverage — introduced important improvements including stateless architecture and enhanced streaming that simplify building MCP servers.
For A2A, the official repository is github.com/a2aproject/A2A. Five official SDKs cover the main languages: Python, TypeScript/JavaScript, Java, .NET, and Go. Since June 2025, the project has been under the Linux Foundation, licensed under Apache 2.0 — ensuring long-term vendor-neutral governance. Among frameworks, Google ADK offers native A2A support and is the recommended entry point for the Google Cloud ecosystem. CrewAI added A2A support in recent releases. LangGraph can interoperate via A2A adapters. AutoGen/AG2 and the OpenAI Agents SDK are also progressing toward A2A compatibility. Most of these frameworks also support MCP, making dual adoption within a single project straightforward. The pragmatic approach: start with MCP to connect your agents to resources, then introduce A2A when your architecture moves to multi-agent mode.
“A2A for agents talking to agents, MCP for agents talking to tools and data.” — Martin Keen, IBM Technology
“It turns out we weren’t competing. We were complimenting.” — Anna Gutowska and Martin Keen, IBM Technology
Frequently asked questions
Who developed A2A?
A2A (Agent-to-Agent) is an open-source protocol initiated by Google, announced on April 9, 2025 at Google Cloud Next. At launch, 50 founding partners had already joined the project, including LangChain, PayPal, Salesforce, SAP, and Atlassian. In June 2025, Google contributed the project to the Linux Foundation, where it is maintained under the Apache 2.0 license by the a2aproject organization. That governance transfer — comparable to what the AAIF did for MCP in December 2025 — ensures open, vendor-neutral evolution of the standard.
Which SDKs and frameworks support A2A?
Five official SDKs are available in the a2aproject GitHub repository: Python, TypeScript/JavaScript, Java, .NET, and Go. Among frameworks, Google ADK (Agent Development Kit) provides native A2A support and is the recommended entry point in the Google Cloud ecosystem. CrewAI added A2A support in recent versions. LangGraph can interoperate via A2A adapters. AutoGen/AG2 and the OpenAI Agents SDK are also progressing toward A2A compatibility. Most of these frameworks also support MCP, making it practical to adopt both protocols within the same project from day one.
Do I need to use both A2A and MCP, or just one?
It depends on your architecture. A single agent accessing tools only needs MCP. Once you add multiple agents that coordinate — especially agents built on different frameworks or operated by different teams — A2A becomes relevant. Most real-world agentic architectures end up using both: MCP for the tool-and-data layer, A2A for the inter-agent coordination layer. There is no conflict between them; they are designed to operate on distinct layers and work together naturally.
Does A2A replace REST APIs between services?
No. A2A builds on HTTP and JSON-RPC 2.0, so your existing REST infrastructure remains valid. What A2A adds on top is specific to agentic architectures: dynamic agent discovery via agent cards, standardized task exchange formats, and structured streaming via SSE. REST alone does not solve dynamic capability discovery or structured task delegation between autonomous agents — that is the precise gap A2A fills.
MCP and A2A converge on the same goal: making agentic architectures composable, interoperable, and maintainable at scale. One structures the relationship between an agent and its resources; the other structures the relationship between agents. The leading frameworks are adopting them, and the Linux Foundation is governing them. Whether you are starting from a single agent and anticipating complexity, or building a multi-agent system from the start, understanding both protocols puts you on the right side of the adoption curve — the one that is defining the infrastructure of agentic AI for the years ahead.
