A Developer’s Guide to A2A: Making Multi-Agent Systems Communicate Across Frameworks and Enterprise Boundaries

As you embark and build more sophisticated intelligent multi-agent systems, we often run into a core challenge: how can agents — each potentially written in a different framework like LangGraph, CrewAI, or Google’s ADK — collaborate seamlessly?

Google’s A2A (Agent-to-Agent) protocol tackles this problem by offering a standardized way for agents to send, receive, and manage tasks. This blog explores how A2A works, how it compares to internal coordination protocols like Anthropic’s MCP, and how developers can adopt it in practice.

Why A2A Matters: Solving Agent Interoperability

A2A solves the interoperability challenge. Most agent frameworks are great at managing internal logic, but they don’t natively play well with others. Without A2A, developers must write custom integrations every time they want agents to cooperate.

A2A provides a universal, HTTP-based protocol that defines how tasks are passed between agents. This allows teams to compose modular, specialized agents into a broader ecosystem without reinventing integration logic each time.

A2A in Action: How It Works

The Two Core Roles

1. A2A Server: Wraps an agent (e.g., a CrewAI crew) behind an HTTP interface. It implements methods like tasks/send and tasks/get, translating A2A task requests into something the agent understands.

2. A2A Client: Any other agent, user interface, or command-line tool that needs to interact with an A2A Server.

The interaction pattern is simple: the client sends a task to the server, which processes it through the agent and responds with updates, results, or requests for clarification.

Building an A2A-Compatible Agent

To make an agent A2A-compliant, you insert an adapter layer, often called a Task Handler or Manager. This layer:

• Intercepts HTTP requests like POST /tasks/send

• Translates incoming A2A JSON into internal agent instructions

• Executes those instructions through the agent

• Responds using A2A-compliant JSON for downstream clients

This architecture keeps the agent logic untouched while enabling interoperability with any A2A-compatible tool or service.

Deploying a CrewAI Agent with A2A on Cloud

Here’s how to make a CrewAI agent A2A-enabled and deploy it:

1. Implement a Task Handler to wrap your CrewAI logic.

2. Expose HTTP endpoints for tasks/send and tasks/get.

3. Handle task lifecycles, including long-running or input-dependent flows.

4. Containerize the service with Docker.

5. Deploy:

• On Cloud Run: Push your container to Artifact Registry and deploy as a managed service.

• On GKE: Create a Kubernetes deployment and expose it via an external service.

The AgentCard provides metadata about each agent’s skills, endpoints, and specializations — enabling dynamic discovery and task routing.

Core A2A Endpoints

• tasks/send: Used to initiate or update a task.

• tasks/get: Used to retrieve status and outputs of an ongoing task.

These endpoints manage tasks throughout their lifecycle, including:

• in-progress

• input-required

• completed

• errored

This state system makes it easy to handle long-running tasks like sourcing candidates or running background checks.

Capability Discovery with AgentCard

The AgentCard is a critical mechanism that allows agents to advertise their skills and availability. For example, a sourcing agent might advertise skills like findCandidates or screenProfiles, while a background check agent might offer verifyEmployment.

When a host agent receives a new user request, it can search the AgentCard directory and dynamically route tasks to the most qualified agent.

Handling Multi-Turn Interactions

Some agent tasks require more input — like location preferences or document uploads. A2A solves this elegantly with the input-required state.

1. The agent halts and requests clarification.

2. The client (UI, user, or another agent) sees the input-required state and prompts for input.

3. Once provided via tasks/send, the task continues.

This mechanism preserves context across turns without spawning new tasks for each user response.

Rich Content Exchange: Beyond Text

A2A supports Rich Content Parts, allowing agents to send and receive more than plain text:

• TextPart: Standard text instructions or responses.

• FilePart: Uploads like resumes, reports, or images.

• DataPart: Structured JSON (e.g., tables, form schemas) for interactive interfaces.

This supports complex workflows, such as submitting forms, reviewing lists, or sharing documents between agents and users.

Managing Long-Running Tasks

For use cases like background verification or report generation, A2A supports persistent task tracking. The agent updates the task’s state via lifecycle statuses, optionally including ETAs or progress markers. Clients poll with tasks/get to check progress.

This pattern avoids blocking and ensures clarity and predictability across distributed agents.

Comparing A2A and MCP: Complementary Protocols

• A2A enables external coordination between agents in different systems.

• MCP (Model Context Protocol) enables internal coordination between modules or sub-agents within one system.

When to use both? Imagine a host agent using MCP to manage internal logic (summarization, ranking, context memory), but also needing to call an external agent (e.g., a third-party legal reviewer). Here, A2A bridges the external boundary while MCP keeps the internal logic organized.

A Unified Vision of Collaboration

The future of multi-agent systems will be built on composability, clarity, and cooperation. A2A offers the plumbing for cross-agent interoperability, while MCP ensures internal cohesion. Together, they pave the way for decentralized, intelligent agent ecosystems that are robust, explainable, and reusable.

Resources to Dive Deeper

• GitHub — google/A2A

• Official A2A Docs

• Awesome A2A (curated agents)

• LangGraph-compatible A2A agent example

Have questions or examples to share? Drop a comment or reach out — let’s build the next wave of intelligent, agentic collaboration.