As autonomous agents become more prevalent in our workflows, the need for agent collaboration is growing fast. On April 9, 2025, Google introduced the A2A Protocol (Agent-to-Agent)—an open standard designed to enable AI agents from different vendors to securely and efficiently communicate, collaborate, and coordinate. Much like APIs made services interoperable, A2A is poised to do the same for AI agents.
In this article, we’ll explore what the A2A protocol is, how it works, and why it’s a foundational piece in the future of intelligent automation.
What Is the A2A Protocol?
The Agent-to-Agent (A2A) Protocol is an open, task-oriented communication standard developed by Google and more than 50 enterprise and technology partners. It allows AI agents—regardless of who built them—to work together seamlessly across platforms, teams, and organizations.
Think of A2A as the language that allows your scheduling assistant, HR recruitment bot, and finance report generator to understand and coordinate with each other—even if they come from different providers.
Why the A2A Protocol Matters
Most AI agents today work in isolation. While powerful, these isolated agents miss the opportunity for collaboration across workflows, which is where real business value lies.
A2A matters because it:
- Breaks silos between agents and tools
- Enables autonomous agents to divide and conquer complex tasks
- Enhances enterprise productivity through smart agent orchestration
- Lays the groundwork for an open, vendor-agnostic AI ecosystem
Just like APIs unlocked web integrations, A2A unlocks multi-agent systems that talk and act like a team.
How the A2A Protocol Works
The A2A protocol is task-based, allowing one “client agent” to request actions from a “remote agent.” Here’s how it works:
Capability Discovery
Agents publish their skills via a JSON-based “Agent Card,” letting others know what they can do.Task Lifecycle Management
Tasks are created, updated, and completed with real-time status updates—ideal for long-running or asynchronous jobs.Contextual Messaging
Agents can send text, structured data, or even UI content as part of a task conversation.Secure and Modular
Built on open standards like HTTP, JSON-RPC, and Server-Sent Events, with enterprise-grade auth via OpenAPI security schemes.Multimodal Flexibility
The protocol supports text, voice, and visual content—making it suitable for rich, UX-friendly agents.
Key Benefits of the A2A Protocol
- Vendor Interoperability – Agents from Salesforce, SAP, Google, or Open Source can all communicate
- Open Source and Community-Led – Built in collaboration with industry leaders
- Support for Long-Running Tasks – Enables agents to manage complex, async workflows
- Secure by Default – Built with enterprise needs in mind
- Designed for Agent-First Workflows – No need for shared databases or monolithic tools
How Does A2A Interact with the Model Context Protocol (MCP)?
While A2A focuses on enabling agent-to-agent collaboration, the Model Context Protocol (MCP) specializes in connecting language models to external tools like file systems, APIs, and databases. Think of it this way:
MCP = Agent ↔ Tool
A2A = Agent ↔ Agent
The two are highly complementary. A single agent might use MCP to translate documents via a tool like Lara Translate, and then use A2A to pass the results to another agent for publishing, summarizing, or reporting.
In the near future, we can expect hybrid systems where MCP-enabled agents participate in A2A networks, sharing insights, results, and tasks dynamically across organizational boundaries.
Real-World Use Cases of the A2A Protocol
Recruitment Automation
An HR assistant agent asks sourcing agents to gather candidate profiles, which are then screened by another agent. A final scheduling agent coordinates interviews—all without human intervention.Marketing Campaign Coordination
A content strategist agent requests copywriting from a language model, passes it to a localization agent (via Lara Translate MCP), and sends the localized output to a campaign management agent for publishing.IT Incident Management
An alert triage agent detects an outage, notifies a remediation agent, and escalates to a documentation agent to create postmortems—all via A2A.
Conclusion
The A2A protocol marks a pivotal step toward an open, cooperative future for autonomous agents. By standardizing how agents communicate, A2A opens the door to cross-vendor collaboration, scalable workflows, and dynamic orchestration—without relying on fragile integrations or custom APIs.
As businesses move toward agent-first architectures, A2A offers the blueprint for building interoperable, intelligent, and secure multi-agent ecosystems.
Whether you’re building with tools like MCP or crafting your first AI workflow, A2A is a protocol worth watching—and implementing.
Useful links
- What Is the Model Context Protocol (MCP)?
- Best MPC Servers for Business in 2025 (Tried & Tested)
- Getting started with Lara Translate MCP Server
- Lara Translate Developer Docs
- Lara MCP Server Github
- Lara MCP Docker
FAQ
Is A2A available today?
Yes, A2A is open source and available for developers to explore and integrate. A production-ready spec is expected later in 2025.
What languages or standards does A2A use?
It’s built on JSON-RPC over HTTP with Server-Sent Events (SSE) for updates and OpenAPI for task schema validation.
Does A2A replace APIs or the Model Context Protocol?
No. A2A complements both. APIs are for app-to-app communication, MCP is for model-to-tool interaction, and A2A is for agent-to-agent collaboration.
What is the A2A Protocol?
The A2A Protocol (short for Agent-to-Agent) is an open standard developed by Google and a coalition of over 50 partners to enable seamless communication between autonomous AI agents—regardless of the vendor, framework, or programming language used to build them. It allows one agent to request help from another by exchanging structured tasks and responses over HTTP using open formats like JSON-RPC. It’s like creating a universal language for agents to collaborate, much like how APIs enabled services to integrate seamlessly over the web.
Why does the A2A Protocol matter for AI development?
In the current AI landscape, many agents are built as standalone tools. While powerful individually, they are limited by their inability to collaborate across applications or platforms. A2A enables distributed agents to work together to complete more complex workflows. For enterprises, this translates into smarter automation, lower integration costs, and increased agility. It brings us closer to the vision of agent ecosystems—where tools can autonomously divide, delegate, and synchronize tasks to get work done without human coordination.
How does A2A compare to the Model Context Protocol (MCP)?
A2A and MCP serve different but highly complementary purposes:
- A2A is built for agent-to-agent communication—allowing two autonomous entities to exchange tasks, responses, and updates in real-time.
- MCP is focused on model-to-tool interaction, allowing a language model to interface with external resources like file systems, APIs, or databases.
For example, an AI agent using MCP might pull data from a spreadsheet or use Lara Translate to convert a document, and then send the result to another agent via A2A for review or publication. Together, these protocols form a powerful backbone for multi-agent, tool-integrated AI workflows.
What are the core technical components of A2A?
A2A is designed to be open, secure, and extensible. Key components include:
- Agent Cards: JSON-based documents that describe an agent’s capabilities, task schemas, and supported modalities.
- Task Lifecycle: Structured workflow that supports creation, progress tracking, and completion of tasks.
- Messaging & Context: Agents can exchange contextual information, results, or interface elements.
- Secure by Default: Authenticated using OpenAPI schemes, with fine-grained control over authorization.
- Modality Support: Works with text, voice, or visual content for richer collaboration experiences.
It’s built on established standards like HTTP, Server-Sent Events (SSE), and JSON-RPC, making it easy to adopt in any modern development stack.
What types of tasks can be coordinated with A2A?
A2A is highly flexible and supports any task that can be broken down into structured inputs and outputs. Common use cases include:
- Document generation and approval workflows
- Conversational handoffs between agents (e.g. chatbot to human handoff agent)
- Content translation and localization (e.g. using Lara Translate via MCP, followed by distribution via A2A)
- Multi-step data analysis (e.g. sourcing → filtering → reporting)
- Cross-department automation (HR, IT, marketing, operations)
As long as an agent publishes a schema for a task and another agent is authorized to send it, the collaboration can happen programmatically.
Is the A2A Protocol open source?
Yes. Google has released the A2A Protocol as an open specification and open source project. Developers and organizations are encouraged to contribute and implement their own agents using the protocol. The initiative is community-driven and follows a transparent governance model to ensure broad compatibility and trust.
You can access the draft specification, examples, and SDKs on Google’s GitHub.
Who is supporting the A2A initiative?
Over 50 technology companies and service providers have joined the A2A initiative, including:
- Tech Vendors: Salesforce, SAP, Box, MongoDB, LangChain, PayPal, Workday
- AI and LLM Tools: Cohere, Google, Intuit
- Consulting and Services: Deloitte, McKinsey, PwC, Accenture, Capgemini, KPMG, TCS
This wide coalition indicates a shared commitment to creating a standardized, interoperable agent ecosystem across industries.
Can I use A2A with popular language models like GPT, Claude, or Gemini?
Yes—as long as the model is wrapped inside an agent that can send and receive structured tasks over HTTP. You could build a Claude- or GPT-powered agent that listens for tasks like “summarize report” or “translate email” and responds using the A2A task lifecycle. Similarly, models running inside tools like LangChain, Claude Desktop, or even MCP clients can participate in A2A workflows.
What’s a real-world example of agents using A2A?
Let’s say a recruiter’s agent wants to fill a new job role:
- It sends a task to a sourcing agent to find candidates from databases and LinkedIn.
- The sourcing agent replies with a short list.
- The recruiter’s agent then sends that list to a screening agent for resume review.
- Once screened, the agent forwards shortlisted candidates to a calendar scheduling agent for interviews.
All of this happens using A2A without needing to build custom APIs between every service. It’s agent-driven workflow automation at scale.
How can I start building with the A2A protocol?
Here’s how to get started:
- Read the official A2A developer guide
- Review the A2A GitHub repository and reference agent examples
- Define your first Agent Card (JSON)
- Build an HTTP server to handle task POST requests
- Start collaborating with other open-source or enterprise agents
This article is about
- What the A2A (Agent-to-Agent) Protocol is and why it matters
- How A2A allows agents from different vendors to collaborate
- How A2A complements the Model Context Protocol (MCP)
- Real-world use cases for agent interoperability
- The technical structure behind A2A
- Key benefits like security, openness, and task orchestration
- How to get started with A2A in multi-agent applications
