Google's A2A Protocol Explained: How AI Agents Talk to Each Other
March 22, 2026
By AgentMelt Team
As companies deploy more AI agents across departments, a critical problem emerges: agents cannot talk to each other. Your sales agent runs on one platform, your support agent on another, and your operations agent on a third. They each have valuable context, but they operate in silos. Google's Agent-to-Agent (A2A) protocol is designed to fix this by creating a standard way for AI agents to discover, communicate with, and delegate tasks to each other regardless of the underlying framework or vendor.
What A2A actually is
A2A is an open protocol that defines how AI agents communicate. Think of it like HTTP for agent-to-agent interaction. It specifies a standard format for agents to advertise their capabilities, accept task requests, report progress, and return results.
The core components:
Agent Cards. Every A2A-compatible agent publishes an Agent Card, a JSON document that describes what the agent can do, what inputs it accepts, what outputs it produces, and how to authenticate. Agent Cards are hosted at a well-known URL (typically /.well-known/agent.json), making agent discovery straightforward. Other agents or orchestrators can read this card and understand how to interact with the agent without any custom integration.
Task objects. When one agent wants another agent to do something, it creates a Task. A Task has a lifecycle: submitted, working, input-required, completed, or failed. Tasks can include text, structured data, files, and streaming updates. The requesting agent can poll for status or subscribe to real-time updates.
Message exchange. Agents communicate through Messages within a Task. Each message has a role (user or agent), one or more Parts (text, data, file), and metadata. This mirrors how humans interact with AI agents but structures it for machine-to-machine communication.
Capability negotiation. Before delegating work, the requesting agent checks the target agent's capabilities via its Agent Card. This prevents sending a data analysis task to an agent that only handles email. If the target agent cannot handle the request, it responds with a structured error rather than attempting and failing.
How A2A differs from MCP
Anthropic's Model Context Protocol (MCP) and Google's A2A solve different but complementary problems. Understanding the distinction is important for architecture decisions.
MCP connects agents to tools. MCP defines how an AI agent accesses external tools and data sources: databases, APIs, file systems, and web services. It standardizes the interface between an agent and the things it uses. Think of MCP as giving an agent hands to interact with the world.
A2A connects agents to agents. A2A defines how multiple AI agents communicate and coordinate with each other. It standardizes the interface between agents working together on a shared goal. Think of A2A as giving agents a shared language to collaborate.
In practice, you need both. An AI agent uses MCP to access its tools (query a database, call an API, read a file) and uses A2A to delegate subtasks to other agents or respond to requests from other agents. A well-architected multi-agent system uses MCP for vertical integration (agent-to-tool) and A2A for horizontal integration (agent-to-agent).
| Aspect | MCP | A2A |
|---|---|---|
| Purpose | Agent-to-tool communication | Agent-to-agent communication |
| Primary use | Data access, API calls, tool execution | Task delegation, coordination, status updates |
| Discovery | Tool manifests within an MCP server | Agent Cards at well-known URLs |
| Statefulness | Stateless tool calls | Stateful task lifecycle |
| Streaming | Supports streaming responses | Supports streaming via SSE |
| Backed by | Anthropic | Google (with 50+ partners) |
Why interoperability matters for business
Without a standard protocol, every agent-to-agent integration is custom-built. A company with 5 AI agents from 3 different vendors needs up to 10 custom integrations (each pair of agents). Add a 6th agent and you need up to 5 more integrations. This scales quadratically and becomes unmanageable.
A2A reduces this to a linear problem. Each agent implements the A2A protocol once, and it can communicate with any other A2A-compatible agent. Adding a new agent to your ecosystem requires zero custom integration with existing agents.
Real-world scenario: A mid-size company has these agents in production:
- A sales agent (built on Salesforce AgentForce) that qualifies leads
- A support agent (built on Zendesk AI) that handles customer issues
- A finance agent (custom-built) that processes invoices
- An operations agent (built on ServiceNow) that manages workflows
Without A2A: When the support agent identifies an upsell opportunity, a human copies the information and manually creates a lead for the sales agent. When the sales agent closes a deal, a human notifies the finance agent to set up billing. Each handoff involves human coordination, delays, and potential errors.
With A2A: The support agent detects an upsell signal and delegates a "qualify-lead" task directly to the sales agent via A2A, passing the customer context. The sales agent qualifies and closes the deal, then delegates a "setup-billing" task to the finance agent. The operations agent monitors the entire flow and flags bottlenecks. All automated, all structured, all auditable.
Building on A2A today
A2A is an open specification with reference implementations in Python and JavaScript. Here is how to start building:
Step 1: Define your Agent Card. Describe your agent's capabilities, supported input/output formats, authentication requirements, and endpoint URLs. Be specific about what tasks your agent can handle and what it cannot.
Step 2: Implement the task endpoint. Your agent needs to accept Task creation requests, process them, update status, and return results. The reference implementations provide server scaffolding for this.
Step 3: Add discovery. Host your Agent Card at the well-known URL so other agents can find and understand your agent. For internal deployments, you can also use a registry service that catalogs all available agents.
Step 4: Implement a client. For your agent to delegate tasks to other agents, it needs an A2A client that can discover agents, read their Agent Cards, create Tasks, and process results.
Key partners and frameworks supporting A2A: Google (Vertex AI, ADK), Salesforce (AgentForce), SAP (Joule), ServiceNow, LangChain, CrewAI, and over 50 other companies have committed to supporting the protocol. This broad adoption significantly increases the likelihood of A2A becoming a de facto standard.
Architecture patterns for multi-agent systems with A2A
Hub and spoke. A central orchestrator agent receives all requests and delegates to specialist agents via A2A. Simple to reason about and debug. Works well when there is a natural coordination point (a support ticket system, a project management tool, a CRM).
Mesh. Any agent can communicate with any other agent directly. More flexible but harder to monitor and debug. Works well for peer-to-peer collaboration where there is no natural hierarchy.
Hierarchical. Team-level orchestrator agents manage their specialist agents, and a company-level orchestrator manages the team orchestrators. Maps well to organizational structure and permission boundaries.
For most businesses starting with multi-agent systems, the hub-and-spoke pattern is the right starting point. It is easier to monitor, debug, and secure. Move to mesh or hierarchical patterns only when the hub becomes a bottleneck.
What this means for your AI strategy
If you are building or buying AI agents today, prioritize vendors and frameworks that support or plan to support A2A. Choosing proprietary, closed agent platforms creates lock-in and makes future agent-to-agent integration expensive.
Short-term (next 6 months): Evaluate your current agent ecosystem. Identify handoffs that are currently manual (human copies data between agents). These are your highest-value A2A opportunities.
Medium-term (6-18 months): Implement A2A for your most impactful agent-to-agent workflow. Measure the reduction in manual coordination time and error rates.
Long-term (18+ months): Build toward an agent mesh where any agent in your organization can discover and delegate to any other agent, with appropriate authentication and authorization controls.
The companies that build interoperable agent ecosystems now will have a compounding advantage as the number and capability of AI agents grows.
For multi-agent architecture fundamentals, see Multi-Agent Systems Explained. For workflow automation strategies, read AI Operations Agent Workflow Optimization. Explore the full AI Operations Agent niche for implementation guides.