Agent2Agent (A2A) Protocol is an open standard for communication and collaboration between AI agents.2 Though it’s new, it’s gaining attention, especially since it works well with MCP, which is becoming the industry standard. A2A is expected to become the go-to protocol for multi-agent communication.
Initially, when Google released the Protocol Specification, it was unclear how to use it. However, with the release of the official Python SDK, it’s now easier to implement. This article will explore how the protocol facilitates communication between agents and clients.
A2A components
Actors and protocols
The A2A protocol defines three main actors:
- User: The end-user who initiates a task.
- Client agent: The entity that requests and sends the task on behalf of the user.
- Remote agent: The agent that performs the task and returns the results.
In the diagram example, the blue agent is the client agent, and the green agent is the remote agent.
Agent cards and discovery
Agents share their metadata and capabilities through a standardized JSON document known as an Agent Card, typically hosted at /.well-known/agent.json. This card includes:
- Name, version, and hosting URL
- Description and service provider
- Supported input/output modalities and content types
- Authentication methods
- List of agent skills with tags and examples
Discovery can be done via DNS, registries, marketplaces, or private catalogs.
Core communication objects
At the core of A2A communication is the Task, a structured object representing a unit of work. Tasks go through various lifecycle states like submitted, working, input-required, or completed.
Each Task contains:
- Messages: Used for communication between the Client and Remote agent.
- Artifacts: Immutable results produced by the remote agent.
- Parts: Self-contained data blocks within messages or artifacts, such as JSON.
How the A2A protocol works?
The below workflow example illustrates how communication flows between the Client and Server in the A2A protocol:
How the system communicates
The system consists of three main participants: the user, the Event Agent, and the Alert Agent.
They communicate using “Messages”, which are just individual exchanges of information in the A2A protocol.
These agents are hosted on A2A Servers that handle incoming requests through HTTP endpoints. Each server has Event queues that temporarily hold information while waiting for responses.
A2A client and server roles
The A2A Client starts the communication process. However, an A2A Server can also act as a Client if two agents need to communicate directly.
EventQueue and tasks
The EventQueue stores various objects, including Messages and Tasks. When an agent needs to fulfill a request, it may create a Task, which represents a goal that requires several steps to complete.
A Task goes through different stages, such as being created (submitted), in progress (working), requiring more information (input-required), completed, or failed. Each Task has a unique ID, making it easy to track its progress.
In this example system, there are two main tasks: detecting an event, handled by the Event Agent, and alerting the user, managed by the Alert Agent.
Tasks often involve multiple exchanges of messages between agents, with each message helping to move the task toward completion.
The Event Agent is wrapped in an A2A Server, which manages how messages are sent and received. When a task is triggered, the server oversees the task’s lifecycle, updates, and communication between agents.
How does A2A differ from MCP?
While A2A and MCP both improve AI interoperability, their scopes differ:
- Model Context Protocol (MCP): Focuses on sharing contextual data between AI models and tools. It enables models to access external knowledge sources and user context, thereby improving their responses.
- Agent2Agent Protocol (A2A): Builds upon MCP by enabling full agent-to-agent task coordination, including messaging, role assignment, and artifact sharing. MCP enhances context-awareness in isolated agents. A2A enables autonomous and collaborative workflows among multiple agents.
Other AI agent communication protocols
While A2A (Agent2Agent Protocol) focuses on task coordination and communication within enterprise environments, several other agent communication standards are emerging. Some of them are:
- ANP (Agent Network Protocol): Promotes open, decentralized agent networks using DID (Decentralized Identity) and secure negotiation.3
- AITP: Built for secure economic transactions between agents, particularly across organizational or trust boundaries.4
- Agora: A protocol enabling LLM-based agents to negotiate how they communicate autonomously.5
- LMOS (Language Model OS): Provides a platform to orchestrate cross-framework interoperability across diverse enterprise agent systems.6
FAQ
What are the business use cases of the A2A protocol?
In certain business cases, agent capabilities should be more comprehensive to perform the necessary tasks. Therefore, businesses may require different agents working together on the same task. Companies can benefit from A2A by enabling collaborative AI agent ecosystems that automate previously manual, multi-actor workflows like:
Customer service automation
First-line support agents handle inquiries and escalate to specialized agents (e.g., billing or technical)
Handoff across time zones for continuous support
Collaboration between bots and human agents
Aiding customer service representatives
Enterprise knowledge management
Research agents collect data
Synthesis agents generate structured reports
Cross-domain agents collaborate on interdisciplinary queries
Supply chain planning
Procurement agents interact with supplier agents under defined constraints
Logistics agents work alongside inventory and forecasting agents
Real-time monitoring and response coordination
Software development
Requirements gathering agents pass tasks to code generation agents
Testing agents report issues to development agents
Documentation agents align with implementation teams
Financial services
Risk assessment agents share findings with investment agents
Fraud detection agents alert transaction processors
Portfolio and market analysis agents coordinate investment strategies
Healthcare coordination
Patient intake agents collaborate with diagnostic support systems
Treatment planners align with insurance and scheduling agents
Follow-up agents handle aftercare and reminders
What is agent interoperability?
The A2A protocol is a key enabler of agent interoperability, enabling intelligent agents to collaborate and achieve common goals.
What technical considerations should businesses know about A2A?
To implement A2A effectively, businesses need to understand some protocol details. A2A is an open protocol, meaning it’s publicly available and designed for broad adoption. However, successful integration requires careful planning around protocol structure, particularly in managing tasks, messages, and lifecycle states.
Security is another key aspect. While A2A facilitates agent interaction, companies must protect sensitive data that may be exchanged during tasks. This makes authorization schemes crucial for ensuring that only authorized agents access specific operations or contexts.
Moreover, deploying A2A across cloud environments involves configuring agents to communicate reliably over web infrastructure, often across different platforms or vendor systems. Although A2A offers flexible integration, it is not a managed service, so businesses must handle infrastructure, agent deployment, and compliance responsibilities internally.
What are the limitations or concerns when using A2A?
While A2A enhances interoperability, it also introduces specific challenges. Businesses should avoid expecting seamless communication without effort; success depends on well-defined agent roles and integration planning. It’s essential to recognize that communication encompasses not only text, but also audio, video, and complex multimedia artifacts. Managing this multi-modal exchange across agents increases implementation complexity.
Additionally, A2A is not built to manage managed services or external orchestration layers by default, so companies must handle service monitoring and availability internally. As organizations adopt multi-agent frameworks during the AI era, teams, such as hiring managers, must consider not only technical skills but also experience in orchestrating several key capabilities from agent design to security, workflow logic, and exception handling.
Although the term “artificial intelligence” is often applied broadly, A2A systems require very specific architectural decisions that go beyond generic AI deployments. For instance, when an agent sends a message, it must conform precisely to A2A’s expected format for task states and contextual replies-any deviation can cause breakdowns in communication.
Further reading
External Links
- 1. https://google-a2a.github.io/A2A/
- 2. Announcing the Agent2Agent Protocol (A2A) - Google Developers Blog .
- 3. GitHub - agent-network-protocol/AgentNetworkProtocol: AgentNetworkProtocol(ANP) is an open source protocol for agent communication. Our vision is to define how agents connect with each other, building an open, secure, and efficient collaboration network f.
- 4. AITP: Agent Interaction & Transaction Protocol | AITP.
- 5. Agora Protocol - Scalable Communication Between Agents.
- 6. What is LMOS? | Eclipse LMOS.
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