TL;DR:

• Organizations deploying multiple specialized AI agents face interoperability challenges due to framework-specific silos.

• Agent-to-Agent (A2A) protocol standardizes communication between AI agents built on different frameworks.

• A2A helps organizations build scalable, modular AI systems that work together and power more complex, cross-functional use cases.

Introduction

Many organizations are deploying specialized AI agents or multi-agent systems (MAS) to handle a wide range of tasks. These systems offer powerful capabilities but often operate in isolation. Each MAS has its own communication approach, which makes integration and substitution difficult without writing custom code. This lack of standardization limits modularity and creates challenges for scaling.

To address this, organizations are now prioritizing interoperability. They understand that agent-based automation can succeed only when agents communicate effectively across different frameworks, cloud environments, and data silos.

Agent-to-Agent (A2A) protocol makes this possible by providing a standard way for agents to communicate and share information. Recognizing these advantages, technology companies such as Microsoft have announced their support for A2A, bringing it to platforms like Azure AI Foundry and Copilot Studio.

Introducing the Agent-to-Agent (A2A) protocol

What is A2A?

Agent-to-agent (A2A) is an open protocol that enables structured communication between AI agents on multiple platforms. A2A provides a standardized language and interaction pattern, enabling one agent to leverage another's functionality as a service.

Developed by Google with industry partners, A2A enables secure exchange of information and coordinated actions among AI agents on various enterprise platforms. Rather than developing separate protocols for every framework, A2A allows agents from different frameworks to interact uniformly.

How does A2A work?

A2A uses a lightweight JSON-based RPC schema on top of standard web protocols like HTTP. Agents provide an HTTP(S) endpoint based on the A2A specification for message exchange. The protocol relies on JSON-RPC 2.0 for request/response and Server Sent Events (SSE) for streaming updates.

This design makes integration easier for developers while accommodating intricate interactions and asynchronous updates. Additionally, A2A is framework-agnostic, so any agent or MAS can easily adopt the spec and become A2A compliant. These features make A2A a complete agent coordination ecosystem, not just a basic messaging pipeline.

As shown in exhibit 1, every A2A agent exposes an “Agent Card” typically hosted at a well-known URL (for example, /.well-known/agent.json). It contains a machine-readable representation of the agent’s identity, capabilities, supported data formats, and authentication needs. This metadata allows dynamic discovery, allowing an orchestrator to query the agent’s card to understand its functions and communication methods.

Agents talk to each other in terms of “Tasks”, a formal unit of work with a well-defined lifecycle (submitted, working, completed, failed, etc.) and include a tracking ID.

Exhibit 1

A2A supports standard HTTP authentication headers or tokens defined in the Agent Card, ensuring secure and auditable communication. It effectively turns every AI agent into a service endpoint (A2A Server) that any authorized client (which could be another agent acting as an A2A Client) can call without needing to understand the internal logic behind the agent’s output.

The agent's internal reasoning, prompts, or tool usage remain abstracted. This allows companies to build powerful multi-agent systems where agents with different capabilities can work together seamlessly, provided they comply with the A2A interface contract.

A2A in action

To demonstrate A2A's interoperability capabilities, we conducted an experiment integrating multiple agent systems built on different frameworks.

Experiment: Integrating AutoGen and CrewAI with Google ADK

In our experiment (see exhibit 2), we deployed three agent systems across two separate servers with basic HTTP connectivity between them:

1. MAS-1: An AutoGen-based multi-agent system designed to analyze and query sales data. This system contained specialized agents working together:

• A Planner creates a plan to answer the user's question.

• An Analyzer creates interactions, SQL, or code to interact with the underlying database and derive insights out of the processed data (e.g., derived KPIs, charts, etc.).

• A Presenter constructs user-facing response to the query.

2. MAS-2: A CrewAI-based research team with:

• A Research agent to gather information.

• A Reporting agent to synthesize findings into comprehensive reports.

3. Agent-3 (Orchestrator): Built using Google's Agent Development Kit (ADK), this agent interprets user queries and intelligently delegates tasks to either MAS-1 or MAS-2 based on the request type.

Exhibit 2

Here, both MAS-1 (sales assist) and MAS-2 (market research) exposes A2A-compliant endpoints, each with an Agent Card describing their capabilities and communication needs. The orchestrator, acting as an A2A client, dynamically reads these agent cards to determine which specialized system was best suited for each incoming task.

When a user submits a query about sales trends, the orchestrator automatically routes the request to the AutoGen-based MAS-1. For research questions, it directs tasks to CrewAI-based MAS-2.

Conclusion

A2A addresses an important need for standardized communication as organizations grow their AI agent deployments. By providing a common language for heterogeneous and distributed multi-agent systems, A2A removes significant barriers to scalability.

For organizations looking to maximize the value of their AI investments, adopting A2A-compliant systems offers a path to more flexible, resilient agent ecosystems that can evolve alongside changing business needs.