What Is the A2A Protocol?
The Agent-to-Agent (A2A) protocol is an open standard that enables AI agents built by different vendors, on different platforms, to communicate and collaborate with each other in real time. Developed by Google and donated to the Linux Foundation in April 2026, A2A functions as a universal communication layer for multi-agent AI systems — allowing an agent from one vendor's platform to hand off a task, share context, and receive results from an agent on an entirely different platform, without custom integration work.
If you are evaluating multi-agent AI deployments, the question your IT director will ask is this: if we deploy agents from Microsoft, Anthropic, and Salesforce in parallel, will they work together — or will we be managing three separate AI ecosystems that cannot communicate? A2A is the standard that answers that question.
Why Does Enterprise AI Now Run on Multiple Agents?
Enterprise AI has evolved beyond single-model assistants. In 2026, the operational pattern that delivers measurable ROI is multi-agent orchestration: specialised agents handling finance, HR, customer service, and compliance in parallel, coordinating handoffs without human intervention. Gartner's 2026 CIO Survey found that 67% of enterprises planning AI investments this year intend to deploy more than three distinct AI agents across different business functions.
The problem is that these agents are rarely built by the same vendor. A financial services firm may run a compliance agent from Palantir, a customer engagement agent from Salesforce Einstein, and a document-processing agent on Anthropic Claude. Without a shared communication standard, each of these agents operates in isolation — unable to pass tasks to each other, share intermediate results, or coordinate on complex workflows that span multiple systems.
The A2A protocol solves the isolation problem by giving all agents a common language, regardless of who built them or which framework they run on.
How Does the A2A Protocol Work?
A2A defines a standardised communication structure built on three established web technologies: HTTPS for secure transport, JSON-RPC 2.0 for message formatting, and Server-Sent Events (SSE) for real-time progress updates during long-running tasks. For enterprise IT teams, this means A2A integrates cleanly with existing security infrastructure — no custom protocols or proprietary connectors required.
The core unit of A2A communication is the Task. A client agent submits a Task to a remote agent — specifying the work to be done, the input data, and the expected output format. The remote agent processes the Task asynchronously and returns status updates and results through a standardised lifecycle: submitted, working, complete, or failed. Crucially, A2A maintains opacity between agents: the client agent cannot see the internal reasoning or tools of the remote agent, preserving intellectual property and security boundaries between vendors.
For enterprise architects, this means A2A is not a replacement for your existing agent orchestration frameworks such as LangChain, AutoGen, or ServiceNow's orchestration layer. Instead, A2A acts as the messaging tier that allows these frameworks to interoperate across organisational and vendor boundaries.
Which Major Vendors Have Adopted the A2A Protocol?
Since Google donated A2A to the Linux Foundation in April 2026, adoption among major enterprise AI vendors has accelerated. IBM has integrated A2A support across its Watson Orchestrate and Think 2026 enterprise agent suite. ServiceNow has announced A2A-compatible agent handoffs as part of its May 2026 Forward Deployed Engineering programme with Accenture. Microsoft Copilot Studio has published A2A compatibility roadmap documentation for Azure-hosted agent deployments.
Critically, the Big Four professional services firms — KPMG, PwC, Deloitte, and EY — are each building enterprise AI platforms that will need to interoperate with client systems. KPMG's Digital Gateway Powered by Claude, announced on May 19, 2026, is expected to support A2A as the firm deploys AI agents across its 276,000-person global workforce and into client environments. For enterprise leaders, this signals that A2A is no longer an experimental protocol — it is becoming the foundation of how enterprise AI agents are delivered and integrated at scale.
What Does A2A Mean for Your Vendor Evaluation Strategy?
A2A fundamentally changes how enterprise leaders should evaluate AI vendors in 2026. The old evaluation framework asked: does this vendor's platform do what we need? The new framework asks: does this vendor's platform interoperate with the other agents we have already deployed, and with the agents our partners, clients, and regulators may deploy in the future?
A vendor that does not support A2A is building a proprietary island. You can deploy excellent AI capabilities within that island, but the island cannot connect to your broader AI ecosystem without custom integration work — which creates vendor lock-in, maintenance overhead, and architectural fragility. According to Forrester's 2026 Enterprise AI Architecture Survey, organisations that deployed multi-agent systems without a shared interoperability standard reported 43% higher integration costs and 28% longer time-to-production compared to those using open standards.
Three questions to include in every AI vendor evaluation from this point forward. First: does your platform support A2A task submission and response for agent-to-agent communication? Second: do you participate in the Linux Foundation's A2A working group or any formal interoperability testing programme? Third: can you demonstrate a cross-vendor agent handoff in a sandbox environment before we commit to a contract?
How Does A2A Differ from Other AI Integration Standards?
Enterprise leaders often encounter three integration terms that are distinct but related: APIs, MCP (Model Context Protocol), and A2A. Understanding where each fits prevents architecture mistakes that become expensive to undo.
A standard REST API connects your software systems to an AI model — it defines how your application sends a prompt and receives a response. MCP (Model Context Protocol) is a newer standard that connects an AI agent to external tools and data sources — allowing the agent to query your CRM, read a document, or write to a database using a standardised connector format. A2A operates at a different layer entirely: it connects AI agents to other AI agents, enabling multi-agent workflows where specialised agents collaborate on complex, multi-step tasks that no single agent could handle alone.
Think of it this way: APIs are the language your software uses to talk to AI. MCP is the language your AI agent uses to access your data and tools. A2A is the language your AI agents use to talk to each other.
What Are the Common Mistakes Enterprise Leaders Make When Planning for A2A?
The most common mistake is treating A2A readiness as a technical problem and delegating it entirely to the IT team. A2A interoperability has direct commercial implications: it determines which AI vendors you can work with, which client platforms your AI outputs can connect to, and whether your AI investment retains value as the industry evolves. This is a strategic decision that belongs in the boardroom alongside vendor selection and data governance.
The second mistake is waiting for full A2A adoption before starting multi-agent deployment. A2A does not require 100% of your agent ecosystem to support it on day one. A practical approach is to identify the two or three agent handoffs that create the most operational friction today, verify that the relevant vendors support A2A for those specific handoffs, and build your architecture around those validated connections. Expand coverage as vendor support matures.
The third mistake is confusing A2A with a security guarantee. A2A standardises how agents communicate — it does not replace your existing identity management, access controls, or data classification policies. Every A2A connection in your enterprise still requires the same security review as any other API integration.
Building Your A2A-Ready Enterprise AI Strategy
The organisations winning in enterprise AI in 2026 are not those who found the best single AI model. They are the organisations who designed an AI architecture that can grow, connect, and adapt as the vendor landscape evolves. A2A is the foundation that makes that architecture possible.
UD has been partnering with Hong Kong enterprises on technology integration for 28 years. The AI landscape moves fast — but the principles of sound architecture and trusted partnerships do not change. With UD, AI works for you — not the other way around.
Ready to Build an Enterprise AI Architecture That Scales?
Understanding A2A is step one. Designing an enterprise-grade multi-agent architecture that is interoperable, secure, and aligned with your business objectives is the work that follows. UD's AI Staff Solution team will walk you through every step — from AI readiness assessment and architecture design to full deployment and performance tracking. With 28 years of enterprise technology experience in Hong Kong, we guide organisations like yours from strategy to production.
