In April 2025, Google has taken a momentous step forward in the field of artificial intelligence with the launch of Agent2Agent (A2A), an open protocol that allows seamless communication between AI agents and
Regardless of their origin or architecture. Developed in collaboration with more than 50 leading technology partners, this protocol represents a significant advancement toward communication standardization between artificial intelligence. The system facilitates the exchange of tasks, data, and decisions in real time using a JSON-structured format, enabling specialized agents to collaborate autonomously to solve complex problems.
Currently in early access, A2A is shaping up to be a transformative technology that will lay the foundation for a more cohesive and efficient AI ecosystem when it officially launches in late 2025.
Does A2A have anything to do with Model Context Protocol?
Yes, Agent2Agent (A2A) and Model Context Protocol (MCP) are related but serve complementary roles in the AI ecosystem:. Remember that I have already investigated MCP in recent content.
Key differences
- A2A: Protocol for communication between AI agents (horizontal), allowing collaboration between autonomous systems.
- MCP: Anthropic’s protocol connects AI models with external (vertical) data, acting as a bridge between applications and models.
Complementary functionality
The protocols can be used together. MCP provides context and tools to individual agents, while A2A enables collaboration between those agents equipped with MCP.
Google recommends sharing A2A agents as MCP resources for more robust integrations.
Combined architecture
An agent using MCP to access enterprise data could communicate via A2A with other specialized agents, creating multi-agent workflows with access to real-time contextual information.
The protocols represent two layers of interoperability: MCP in model-data interaction and A2A in agent-agent collaboration.
The evolution towards Agéntic AI
While recent years have been dominated by the development of large language models (LLMs) by companies such as Google, OpenAI, Antrophic and Microsoft, 2025 marks the beginning of a new era: that of Agentic AI deployments in enterprise environments.
Agentic AI differs from traditional approaches by incorporating unprecedented autonomy and specialization. Each agent is designed to excel in a specific area of knowledge or functionality, intervening based on its expertise when necessary and communicating with other agents to ensure a consistent and optimized outcome. This distributed approach improves efficiency and enables scalable solutions to problems that previously required significant human intervention.
Context of this Google release
Google’s launch of Agent2Agent comes at a critical time in the evolution of enterprise artificial intelligence. As organizations deploy multiple AI platforms and frameworks, there is a pressing need for communication standards to ensure interoperability.
A2A’s announcement was made during Next ’25, Google Cloud’s annual event, as part of a broader strategy to drive the Agentic AI ecosystem forward.
What is Agent2Agent and how does it work?
Agent2Agent (A2A) is an open protocol specifically designed to facilitate seamless communication between artificial intelligence agents, even when they have been developed with different technologies or by different vendors. Its fundamental goal is to become a universal language that enables AI systems to communicate, collaborate, and coordinate actions efficiently.
The protocol seeks to solve one of the biggest challenges in enterprise AI adoption: interoperability between systems that traditionally operate in isolation. By establishing a common standard, A2A enables enterprises to realize the full potential of their AI investments without the need to develop custom solutions for each integration point, thereby accelerating adoption and reducing associated costs.
Operating Architecture: Client Agent and Remote Agent
The operation of A2A is based on a model of interaction between two types of agents: the “client agent” and the “remote agent.” The client agent is responsible for formulating and communicating tasks from the end user, while the remote agent interprets these requests, executes them, and provides the corresponding responses. This separation of responsibilities allows for efficient specialization and a clear distribution of work.
The architecture allows agents to work “in their natural unstructured modes, even when they do not share memory, tools and context”. Different agents can collaborate effectively without sharing the same code base or infrastructure, greatly facilitating integration between heterogeneous systems in complex enterprise environments.
Format and Technical Standards
Interaction between agents in the A2A protocol uses a standardized format known as “Agent Card”, structured in JSON. This choice is not accidental: by building on existing standards such as HTTP and JSON, A2A facilitates integration with current technological infrastructures and guarantees an adequate level of security by default.
The protocol also complements other standards, such as the Context Protocol Model (CPM), and is compatible with web interfaces, such as iframes and forms. This compatibility allows agents to generate “artifacts” or structured results after each operation, facilitating other systems or agents’ subsequent processing.
One of the most essential features of A2A is the capability discovery mechanism, which allows agents to advertise their skills and specializations through agent cards in JSON format. This system makes it easy for the client agent to determine which remote agent is best suited to complete a specific task, thus maximizing the process’s efficiency.
The protocol allows agents to exchange tasks, data, and decisions in real time, accelerating complex workflows in corporate environments. This real-time collaboration capability represents a significant advance over traditional AI systems, which typically operated as isolated “black boxes” with no ability to interact dynamically with other systems.
In the image above we can seean example of an Agent Card in Google’s A2A protocol, taken from an agent that manages reimbursement processes:
Strategic Partnerships in Development
Google’s collaboration with more than 50 top-tier technology partners has made the development of Agent2Agent possible. These companies include prominent names such as Salesforce, SAP, PayPal, Deloitte, McKinsey, Atlassian, Box, Cohere, Intuit, LangChain, MongoDB, ServiceNow, UKG, and Workday. This broad coalition of partners reflects the technology industry’s strategic importance on artificial intelligence interoperability.
Salesforce’s CEO, Marc Benioff, attended the Google event and announced the collaboration between the two companies.
Salesforce’s Agentforce runs on Google Cloud infrastructure, making it easy to integrate with services such as Vertex AI and potentially with A2A, as this protocol is part of Google’s agentic AI ecosystem.
Agentforce already includes autonomous agents that take specific actions (sending communications, handling queries), a paradigm compatible with A2A’s client-remote architecture, where agents collaborate in distributed workflows.
Agentforce runs on Google Cloud infrastructure, which makes it easy to integrate with services such as Vertex AI and potentially A2A, as this protocol is part of Google’s agentic AI ecosystem
Launch of A2A
Agent2Agent is currently in the early access phase, allowing developers and early adopters to explore its capabilities and begin integrating the protocol into their systems. According to available information, A2A is scheduled for official release in late 2025, suggesting a period of refinement and optimization based on feedback received during the early access phase.
Agent2Agent represents a fundamental breakthrough in the evolution of enterprise artificial intelligence, establishing an open and comprehensive standard for communication between intelligent agents for the first time. Its careful design, based on collaboration with multiple industry partners and built on proven technical standards, positions it as a strong candidate to become the reference protocol for agentic AI for years to come.
Google’s emphasis on openness and community collaboration in the development of A2A reflects a recognition that the future of AI will not be dominated by monolithic systems but by ecosystems of specialized agents working in concert.
Enterprises and developers have a unique opportunity to position themselves strategically in this emerging Agentic AI ecosystem. Those who harness the potential of A2A to create innovative and efficient solutions could find themselves at the forefront of the next revolution in artificial intelligence.
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