Google’s Agent2Agent (A2A) protocol: A new standard for AI agent collaboration
Discover Google's Agent2Agent (A2A) protocol, a new open standard enabling seamless collaboration between AI agents. Learn how A2A enhances interoperability, drives efficiency, and transforms enterprise workflows.
Created on April 9|Last edited on April 9
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Google has introduced Agent2Agent (A2A) – a newly announced open protocol designed to let AI agents talk to each other directly, no matter who built them, what platform they run on, or which framework they use. In an enterprise world where organizations are deploying autonomous AI agents for everything from IT procurement to customer support, A2A aims to be a “lingua franca” that allows these agents to collaborate as a team rather than operate in isolation. Announced on April 9, 2025 with backing from over 50 industry partners including Atlassian, SAP, Salesforce, ServiceNow, LangChain, Cohere, and of course - Weights & Biases - A2A is poised to standardize how autonomous AI systems interact in real-world workflows. This deep-dive article explains what A2A is, how it works, and why it’s important – for both business leaders looking at strategic impacts and developers seeking technical insights.
Table of contents
What is Agent2Agent and why does it matter?Why is this important now?Agent2Agent’s core principles and architectureHow Agent2Agent worksAddressing enterprise interoperability challenges with A2AReal-world use cases enabled by Agent2AgentHiring and candidate sourcing workflowsCustomer service and support automationCross-enterprise process integrationBusiness and market impact of Agent2AgentIndustry partner reactions and ecosystem supportGetting involved with Agent2AgenF: for developers and businessesConclusionResources
What is Agent2Agent and why does it matter?
Agent2Agent (A2A) is an open interoperability protocol for AI agents. Its core purpose is to provide a standard way for agents – possibly built by different vendors or using different AI frameworks – to communicate, share information, and coordinate actions. A2A defines a common language and set of rules so that an agent from one system can understand and work with an agent from another system. By doing so, it breaks down the silos between standalone AI agents and enables them to form dynamic “teams” that can tackle complex, multi-step tasks across an organization’s software landscape.
Why is this important now?
Enterprises today are increasingly adopting AI agents to automate and assist with daily tasks, from “ordering new laptops, to aiding customer service representatives, to assisting in supply chain planning”. However, without a common protocol, these agents tend to be isolated – each might be tied to a specific vendor’s platform or a specific function. Google observed that to maximize the benefits of agentic AI, agents must be able to collaborate in a multi-agent ecosystem that spans siloed data systems and applications. Enabling such interoperability “even if they were built by different vendors or in a different framework” will significantly increase productivity and autonomy while lowering long-term costs. In other words, Agent2Agent is meant to unlock network effects: an ecosystem of AI agents working together can achieve more than any single agent could alone, leading to greater efficiency and innovation across business processes.
Crucially, Agent2Agent is launching as a truly open standard. Google developed it in collaboration with dozens of partners and has open-sourced the protocol specification and reference implementations. This openness means that any organization or developer can adopt A2A, contribute to its evolution, or build compatible agents – without being locked into a single vendor’s ecosystem. Google explicitly designed Agent2Agent based on real-world challenges encountered when deploying large-scale, multi-agent systems for customers. The protocol gives developers the flexibility to connect any A2A-compliant agents together and gives businesses a standardized method to manage diverse agents across platforms and cloud environments. Google calls this “universal interoperability … essential for fully realizing the potential of collaborative AI agents”.
It’s worth noting that Agent2Agent is meant to complement another emerging standard: Anthropic’s Model Context Protocol (MCP). Whereas Agent2Agent focuses on agent-to-agent communication, Anthropic’s MCP provides a standardized way for AI assistants (like large language models) to securely connect to external data sources and tools. In Google’s words, “A2A is an open protocol that complements Anthropic's MCP, which provides helpful tools and context to agents”. One tech commentator explained the difference with an analogy: if MCP is the “socket wrench” that lets an AI model plug into data or APIs, A2A is the conversation between mechanics as they work together. In other words, MCP standardizes how an agent uses tools and data, whereas Agent2Agent standardizes how multiple agents coordinate with each other. Together, these efforts address two sides of the interoperability challenge and signal a broader industry move toward open standards for AI systems.
Agent2Agent’s core principles and architecture
Agent2Agent’s design principles were shaped with input from many partners to ensure the protocol meets enterprise requirements. Google outlines five key tenets that guided A2A’s development:
- Embrace agentic capabilities: Rather than constraining one agent to simply call another as a subroutine, Agent2Agent enables agents to engage each other in rich, unstructured ways (e.g. sending free-form messages, not just API calls). Agents can collaborate without sharing memory or tools, meaning each agent can remain an independent entity while still working together. This principle ensures we get true multi-agent collaboration instead of treating other agents as mere “functions.”
- Build on existing standards: Agent2Agent is built atop familiar web technologies – specifically HTTP for requests, Server-Sent Events (SSE) for streaming updates, and JSON-RPC for structured calls. By leveraging these popular standards, A2A can be easily integrated into existing IT stacks and take advantage of proven, widely supported protocols. This reduces the learning curve for developers and avoids “reinventing the wheel.”
- Secure by default: From day one, Agent2Agent is designed with enterprise-grade security in mind. It supports robust authentication and authorization schemes equivalent to what OpenAPI offers for secure APIs. In practice, this means organizations can enforce identity and access controls on inter-agent communications, ensuring that only authorized agents can converse and that sensitive data remains protected.
- Support long-running tasks: Not all tasks that agents handle are quick; some could involve workflows that span hours or even days (especially if human approval loops are involved). Agent2Agent explicitly supports long-lived tasks with a defined task lifecycle, including real-time status updates, progress notifications, and partial results streaming. An agent interaction might begin and then continue asynchronously as agents work through a complex process, with the protocol ensuring they can stay in sync on task status.
- Modality agnostic: While much of today’s AI agent communication is text-based, Agent2Agent is built to handle multiple data modalities. The protocol can carry not just text, but also other content types like images, audio, or video streams. This is important as agents evolve to handle diverse media – for example, one agent might generate a chart or an image as a result for another agent. A2A’s messages include metadata about content format so that agents can negotiate the best way to exchange and present information.
With these principles in place, Agent2Agent’s architecture defines how agents actually interact. At a high level, A2A communication always happens between two roles: a client agent and a remote agent. The client agent initiates the conversation by formulating a task or request, and the remote agent acts on that request to produce a result or perform an action. In many scenarios, the client agent represents the end-user’s side (e.g. the agent a user directly asks to do something) and will seek help from a remote specialist agent to fulfill that request. However, these roles are fluid – any agent can be a client in one interaction and a remote agent in another, depending on the task at hand.
When a client agent needs something done, it creates a Task – a structured object that describes the goal or query. The task gets sent over the Agent2Agent interface to the chosen remote agent, which then works on it and eventually returns a result (or artifact, in A2A terminology). Every task has a defined lifecycle: it can be completed immediately in one shot, or it can remain open while the agents continue exchanging information and updates. For example, if the task is complex, the remote agent might send intermediate progress or ask clarifying questions, and the client agent can provide additional input – all tracked as part of the same task context.
How Agent2Agent works
Several key capabilities are built into the Agent2Agent interaction model to support this workflow:
- Capability discovery: How does an agent know which other agent might be able to handle a given task? Agent2AgentA2A introduces an “Agent Card” – essentially a profile in JSON format that an agent can publish to advertise its skills and endpoints. Using these standardized Agent Cards, a client agent can dynamically discover which remote agent is best suited for a job. This discovery mechanism is crucial in a large enterprise where there could be dozens of specialized agents available.
- Structured task management: As mentioned, tasks have a lifecycle and produce outputs called artifacts. The Agent2Agent protocol defines how tasks are created, updated, and concluded. If a task is long-running, A2A supports keeping the conversation open – the remote agent can periodically send status updates (using SSE streaming events) so the client knows the task is still in progress. The client and remote agent remain in sync regarding task state (e.g. “10% complete”, “awaiting approval”, “completed with result X”). By formalizing task management, A2A ensures that even prolonged, multi-step workflows can be handled methodically between agents.
- Collaboration via messaging: Beyond the raw task data, agents might need to exchange supporting information – context, partial answers, follow-up questions, etc. Agent2Agent allows agents to send free-form messages to each other as part of the task conversation. These messages can include additional context or clarifications, artifacts (actual results like documents, images, data), or even user instructions if one agent needs the user’s guidance on something. This messaging layer means agents truly converse back-and-forth, rather than just a one-off request/response – enabling a more interactive collaboration style.
- User experience negotiation: One particularly novel feature is that Agent2Agent built in a way for agents to negotiate how content is presented or formatted for the end user. Each message an agent sends can be composed of one or more “parts”, where each part is a piece of content with a specific MIME type or format. For example, a remote agent’s answer might include a text summary as one part and a chart image as another part. The client agent can indicate what formats it can handle. The two agents effectively negotiate the best way to deliver the result to the user, given the user’s interface capabilities. It’s a way of aligning on the user experience so that the outcome of an agent-to-agent collaboration can be presented properly, regardless of interface.
The beauty of Agent2Agent’s architecture is that it doesn’t require agents to share their internal state or logic with each other – agents remain “opaque” to one another, exposing only what they choose via the protocol. This is key for enterprise scenarios where an agent might be a proprietary system or have sensitive logic; A2A lets it collaborate with others without revealing its internals. Instead of syncing internal memory, agents share context through well-defined tasks and messages. Google designed A2A to accommodate these opaque agents for the sake of security, modularity, and allowing interoperability even when vendors want to keep certain IP or data encapsulated. This approach to tasks and messages provides the needed abstraction such that agents can work together while still respecting boundaries and trust – a must-have for real enterprise use cases.
Addressing enterprise interoperability challenges with A2A
Agent2Agent directly tackles some of the biggest challenges in enterprise AI agent interoperability:
- Cross-vendor integration: Enterprises might use AI capabilities from multiple providers (Google, Salesforce, ServiceNow, etc.). Historically, getting these systems to talk to each other required custom “glue” code and integrations. Agent2Agent offers a vendor-neutral protocol so that an agent from one vendor can connect with an agent from another out-of-the-box. This dramatically reduces the engineering effort to bridge systems. By solving interoperability at the protocol level, A2A lowers the barrier for businesses to mix-and-match the best AI agents from different sources and have them work in concert.
- Siloed data and processes: In many organizations, data and workflows are fragmented across different applications. An AI agent operating in one application might hit a wall when information or actions are needed from another. Agent2Agent enables what Google calls “multi-agent ecosystems across siloed systems”, allowing an agent to fetch information or trigger actions via a partner agent that has access to those other silos. For instance, if a customer support chatbot needs billing info from an ERP system, it could delegate to an ERP agent via A2A. The result is end-to-end automation across systems that previously didn’t communicate well, which helps break down organizational silos.
- Heterogeneous tech stacks: Enterprises have diverse technology stacks – some on-premises, some cloud; some modern APIs, some legacy systems. Agent2Agent’s use of ubiquitous web protocols (HTTP/SSE) means it can be implemented in virtually any environment that supports web requests. It doesn’t require special network overlays or proprietary middleware. Additionally, by open-sourcing A2A, Google encourages many programming languages and platforms to have A2A client/server libraries or SDKs. This broad compatibility helps ensure that regardless of tech stack, enterprise developers can enable their agents for A2A with minimal friction.
- Security & compliance: A huge concern in enterprise AI adoption is controlling access and ensuring compliance with data policies. Agents that run wild, talking to whomever and sharing data freely, would be unacceptable. Agent2Agentaddresses this by integrating with standard auth schemes and allowing enterprises to apply access control lists or token-based authentication to agent interactions. Companies can enforce which agents are allowed to communicate and log/audit those interactions. Furthermore, because A2A is an open spec, it can be reviewed and vetted for security by the community, and organizations can host A2A services internally to meet data residency or air-gap requirements. All of this gives CIOs and CISOs more confidence that an interoperable agent ecosystem can still be locked down and governed properly.
- Long workflow continuity: Before Agent2Agent, if one AI agent needed another to help on a lengthy task, the hand-off could be clunky – possibly dropping context or requiring a human to intermediate. With A2A’s structured task lifecycle and streaming updates, agents can maintain a shared context for extended collaborations. Consider an incident management scenario: a monitoring agent detects an issue and asks a diagnostic agent to investigate. That analysis might take hours, but thanks to A2A, the monitoring agent can stay updated continuously and even report status to humans in the loop, all while the diagnostic agent works. It’s a protocol built for resilience and continuity, which is exactly what enterprise processes require.
In summary, Agent2Agent confronts the interoperability problem with an approach similar to what HTTP did for the internet – provide a universal language that any compliant system can use to communicate. By addressing technical barriers (different vendors, formats, security requirements) and giving a common framework, it paves the way for AI agents to interoperate broadly in the wild.
Real-world use cases enabled by Agent2Agent
What kinds of things become possible when AI agents can seamlessly talk to one another? Agent2Agent opens up many practical enterprise use cases that require multiple agents or systems to coordinate. Here are a few concrete scenarios:
Hiring and candidate sourcing workflows
One example Google highlights is using Agent2Agent in the hiring process. Talent acquisition often involves several steps: sourcing candidates, screening resumes, scheduling interviews, and conducting background checks. Instead of a single monolithic AI trying to handle all of this, A2A allows specialized agents to each tackle part of the workflow and pass the baton as needed. For instance, a hiring manager could instruct their HR assistant agent to find candidates for a software engineering role. That agent might reach out to a recruiting agent (perhaps one connected to LinkedIn or an internal resume database) to pull a list of qualified candidates. Once candidates are identified, the HR agent could then engage an interview scheduling agent to coordinate times with the hiring team and the candidates. After interviews, a background check agent might get involved to verify credentials. All these agents work in concert through Agent2Agent, sharing the task context so that the overall hiring process moves forward smoothly. The hiring manager simply sees the results at each stage while under the hood A2A facilitated a team of AI agents.
Customer service and support automation
Customer service is another domain ripe for multi-agent collaboration. Consider a customer’s support request that involves multiple systems: the customer chats with a support chatbot (Agent A), but resolving the issue might require actions in the billing system, checking inventory in a database, and escalating a complex question to a knowledge-base search agent. With Agent2Agent, that primary support chatbot can seamlessly invoke other agents to handle sub-tasks. For example, if a customer asks “Why was my credit card charged twice?”, the chatbot (Agent A) could use A2A to ask a billing agent (Agent B) to pull up the transaction records. Agent B might return an artifact with the billing details, which Agent A can present to the customer. If the customer then asks to initiate a refund, Agent A could call a refund-processing agent (Agent C) to execute that transaction in the financial system. Meanwhile, for a complex technical question, Agent A could consult a knowledge base agent (Agent D). Throughout this, Agent2Agenthandles the routing of messages and data between these agents. The customer experiences a single, coherent conversation, but behind the scenes multiple AI specialists collaborated. ServiceNow sees A2A as key to more efficient, connected support experiences by connecting different support agents and tools, leading to faster and more connected customer service.
Cross-enterprise process integration
Enterprises have many end-to-end processes that cross through several departments and software systems. For example, employee onboarding involves HR systems (for paperwork and setup), IT systems (for equipment and access provisioning), and finance systems (for payroll). Today, these hand-offs often involve emails or manual data entry between teams. With Agent2Agent, an “onboarding coordinator” agent could orchestrate the whole workflow by invoking department-specific agents in sequence. When a new hire is entered into the HR system, that HR agent notifies the IT provisioning agent to set up accounts and hardware, which in turn might call a facilities agent to prepare workspace, and a finance agent to set up payroll – all through A2A interactions. Each agent completes its task and updates the shared onboarding task state, so the coordinator agent (and the hiring manager) can see progress in real time. This kind of enterprise system integration through agents can dramatically speed up processes and eliminate human error from re-keying info.
Another example is in supply chain and operations. For instance, fulfilling a customer order might involve an order management agent, an inventory agent, and a shipping/logistics agent. If one item is out of stock, the inventory agent could proactively invoke a procurement agent to reorder it or a recommendation agent to suggest an alternate product – coordinating with the order agent so the customer is kept informed. Without Agent2Agent, each of these agents would be confined to its own system, but with A2A they form an agile supply chain team. Essentially, routine cross-system interactions can be handled by agents talking to agents, freeing human workers to concentrate on exceptions or improvements.
These scenarios barely scratch the surface. We can imagine AI agents for compliance checking, report generation, scheduling, and more, all chaining together. The common thread is that Agent2Agent enables specialization plus coordination: each agent can specialize (doing one thing well, with its own model or logic), and A2A makes them coordinate their specialized skills to achieve bigger objectives. This mirrors how human teams work in organizations, and it’s why many see Agent2Agent as a foundation for an AI-driven “digital workforce.”
Business and market impact of Agent2Agent
The introduction of Agent2Agent could have far-reaching impacts on businesses and the broader AI software market. Here are several key implications:
- Enhanced productivity and efficiency: By enabling autonomous agents to collaborate and handle multi-step workflows, Agent2Agent2A can significantly boost productivity. Tasks that used to require numerous human touchpoints or swivel-chair work between applications can be completed faster and with minimal human intervention. This translates to employees spending less time on administrative coordination and more on higher-value creative or strategic work. It could also mean faster response times for customers and the ability to scale operations without linear increases in headcount.
- Cost reduction: Standardizing agent interactions can cut costs on multiple fronts. Development costs come down because organizations no longer need to build custom integrations for each pair of systems or agents – adhering to Agent2Agent is often enough. Maintenance burdens also drop since a single protocol can be updated rather than dozens of brittle connectors. Additionally, by avoiding vendor lock-in, companies might gain pricing leverage and flexibility when mixing AI solutions from different providers.
- Innovation and new services: An open ecosystem of interoperable agents can spur innovation in much the same way open web standards did. With Agent2Agent, third-party developers can create new agents that plug into existing workflows without needing permission from incumbents. This could lead to a flourishing marketplace of specialized AI agents, each ready to integrate seamlessly via A2A. Companies that embrace it early may shape its evolution and find novel uses, giving them a competitive edge.
- Interoperability as a selling point: As AI vendors rally around Agent2Agent, interoperability will become key in enterprise purchasing decisions. Businesses will prefer AI tools that “play well with others” over closed systems. The fact that giants like SAP and Salesforce are on board indicates that interoperability is crucial for delivering end-to-end value to customers. We may soon see “A2A-compliant” as a mark of readiness to join a broader agent ecosystem, akin to “OAuth compatible” in the identity world.
- A coherent “digital workforce”: With Agent2Agent, the vision of a digital workforce – multiple AI agents with different roles collaborating like a human team – becomes more tangible. Companies could orchestrate these agents to handle processes in parallel, 24/7, with minimal oversight. One could imagine an enterprise assigning an “AI team” to each project, where each AI agent has a specialty. The productivity and output of these AI teams could drastically change how work gets done, and organizations that effectively manage this digital workforce will likely outpace those that don’t.
Industry partner reactions and ecosystem support
One measure of Agent2Agent’s significance is the broad coalition of industry players endorsing and contributing to it from the start. More than 50 organizations – spanning software companies, AI startups, and global consulting firms – joined as partners in shaping A2A. Here are some notable reactions from key partners, highlighting what they find promising about A2A:
- Atlassian: Views Agent2Agent as a way to boost complex collaboration among AI-driven workplace assistants, enabling agents to delegate tasks among themselves at scale.
- SAP: Emphasizes how Agent2Agent will allow their AI solutions to integrate across enterprise platforms, critical for customers running heterogeneous application landscapes.
- ServiceNow: Highlights how Agent2Agent can improve support and IT service workflows by connecting different support agents and tools, leading to faster and more connected customer service.
- LangChain: A popular framework for building AI agent applications, noting that multi-agent interaction is imminent and A2A helps meet developer needs.
- Cohere: Stresses that Agent2Agent enables seamless collaboration while maintaining control and compliance – even in environments with strict data requirements.
- Salesforce: Frames Agent2Agent as an extension of its open platform, allowing AI agents to work with others to create integrated solutions and deliver an “enhanced digital workforce.”
These endorsements, among many others, signal a strong industry consensus that agent interoperability is crucial and that Agent2Agent is a viable path forward. For a business leader, this broad support suggests that investing in A2A compatibility is unlikely to be a dead-end. If A2A gains traction, it could become the underlying fabric of enterprise AI systems in the coming years.
Getting involved with Agent2AgenF: for developers and businesses
With Agent2Agent still in its early days (launching in draft specification form), there is ample opportunity for both developers and organizations to engage and help shape this open standard.
For developers: Google has released the full Agent2Agent specification as open source alongside code samples and SDKs. A great first step is to review the draft spec to understand the data formats (Agent Cards, Task schema, message structure) and protocols (HTTP routes, JSON-RPC, SSE) that A2A uses. From there, you can try out the provided examples to see A2A in action. The community is encouraged to contribute ideas and improvements by submitting feedback, building integrations, or proposing changes to the spec. A production-ready v1.0 release is expected later in 2025.
For businesses and IT leaders: Now is a good time to identify where multiple AI or automation tools are used in sequence and could benefit from talking to each other. A pilot Agent2Agent implementation can help your team see the capabilities and limitations. You can also work with vendors that plan to offer A2A connectors or features. From a strategic standpoint, signaling support for open interoperability in AI will help future-proof your ecosystem. Early adopters may gain a competitive edge by orchestrating AI-driven processes that others can’t easily replicate without the interoperability piece. As Google and partners roll out polished A2A-based tooling or managed services, participating in previews or pilot programs can minimize your investment risk and maximize learning.
Conclusion
Google’s Agent2Agent (A2A) protocol represents a significant step toward an interconnected AI future – one where autonomous agents can cooperate across company and software boundaries to achieve complex goals. By providing a common framework for communication, A2A tackles the fragmentation that has limited the impact of AI agents in enterprises. For business leaders, Agent2Agent promises greater ROI from AI investments: it can unlock new efficiencies, reduce integration costs, and catalyze innovation by enabling a plug-and-play ecosystem of AI capabilities. For developers, A2A offers an exciting opportunity to build solutions that are inherently more interoperable and to help shape a standard that could become foundational in the AI industry.
It’s still early days, and challenges will undoubtedly emerge as the standard matures. But the momentum behind Agent2Agent is a strong indicator that the industry craves a solution to agent silos. Past tech eras saw open protocols (from TCP/IP to HTTP) unlock enormous value, and the AI era may be defined by protocols like A2A that allow our growing menagerie of intelligent agents to work in concert.
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