As businesses increasingly deploy AI agents across their workflows, one challenge becomes clear: agents often struggle to coordinate with one another. This limits their ability to execute multi-step, cross-functional tasks without human intervention. To address this, various protocols have been developed, and Agent2Agent (A2A) is one of these protocols.
What is the Agent2Agent (A2A) protocol?
The Agent2Agent (A2A) protocol is an open standard that enables the interoperability of AI agents, allowing them to communicate and collaborate effectively. The A2A protocol was designed by Google and its over 50 partners.1
The protocol is based on web technologies, including HTTP, JSON-RPC, and Server-Sent Events, making it easy to implement and integrate with existing systems.
The protocol supports interactions between:
- Client agents, which formulate tasks
- Remote agents, which execute them
It is designed for modality-agnostic communication across text, audio, and video, and handles both immediate and long-running operations.
Key features of the A2A protocol
- Agent cards: JSON-formatted profiles that advertise agent capabilities
- Structured task lifecycles: With states like pending, in progress, and completed
- Message exchanges: Including context, replies, user instructions, and artifacts (e.g., images or generated code)
- Modular content parts: Messages may include discrete parts such as summaries, links, or images
Agent collaboration and task management
A2A consists of a standardized task management system that allows agents to:
- Create, update, and track tasks
- Assign responsibilities to other agents
- Share context to support joint task execution
This enables the development of complex workflows that rely on multiple agents with specialized functions, without losing state or coherence across conversations.
Multi-agent systems
The A2A protocol addresses common challenges in multi-agent collaboration:
- Ensures shared understanding among different agents
- Maintains conversation state across task lifecycles
- Coordinates specialized agents for complex, multi-step tasks
- Supports coherence in messaging and role transitions
By enabling standardized communication and shared task states, A2A creates the foundation for a scalable multi-agent ecosystem.
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.2
- ACP (Agent Connect Protocol): Developed by Agntcy.org for managing server-hosted agent interactions with features like thread states and interrupts.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
These protocols serve different needs, ranging from open networks to web discoverability, and businesses may adopt more than one, depending on the use case complexity and system architecture.
How does A2A benefit businesses?
- Workflow automation: Multiple agents handle distinct steps in a process
- Efficiency: Task coordination reduces manual intervention
- Scalability: Additional agents can be added seamlessly
- Innovation: Businesses can test new agent roles and configurations
By leveraging the A2A protocol, companies can orchestrate more powerful and autonomous AI-driven operations. Agent interactions are powered by the A2A protocol, enabling agents to handle more complex tasks.
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. Announcing the Agent2Agent Protocol (A2A) - Google Developers Blog .
- 2. 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.
- 3. Agent Connect Protocol.
- 4. AITP: Agent Interaction & Transaction Protocol | AITP.
- 5. Agora Protocol - Scalable Communication Between Agents.
- 6. What is LMOS? | Eclipse LMOS.
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