There is a pattern in enterprise AI deployments that almost every technology leader will recognise. The first agent works well. The second agent works well. Then someone asks why the two cannot talk to each other — and the answer turns out to be weeks of bespoke integration work, fragile API glue, and a growing maintenance burden that quietly consumes the efficiency gains the agents were supposed to deliver.
This is the integration tax. And until recently, it was simply the cost of doing business in a multi-vendor AI environment.
But things have been getting better. The A2A Protocol is now backed by more than 150 organisations and managed by the Linux Foundation's Agentic AI Foundation. Microsoft, AWS, SAP, ServiceNow, Workday, and IBM are all on board. This is not a proprietary standard competing for dominance. It is quickly becoming the TCP/IP of agentic AI.
The A2A Protocol: 5 Minute Primer
The A2A Protocol defines a standard way for AI agents from different vendors to discover each other, communicate, and hand off tasks — without either system needing to understand the other's internal architecture.
In practice, this means a Salesforce Agentforce agent handling a customer escalation can hand off a data retrieval task to a Google Gemini Enterprise agent, which in turn queries a ServiceNow agent for ticket history — all in a single workflow, without custom code connecting them. Each agent speaks A2A; everything else is handled by the protocol.
The technical mechanism is straightforward: agents expose an Agent Card — a structured description of their capabilities, available actions, and authentication requirements. Orchestrating agents discover these cards, compose workflows, and delegate subtasks. Responses flow back through the same protocol layer. From a developer's perspective, integrating a new agent is closer to calling an API than building a custom connector.
Crucially, data does not need to move between systems for agents to collaborate. A Salesforce agent can instruct a ServiceNow agent to retrieve information and act on it — without that data ever leaving the ServiceNow environment. For enterprises operating under strict data governance or residency requirements, this is significant.
Why This Helps Enterprise AI Architecture
A2A has been around for a while, but getting The conventional approach to multi-agent enterprise AI has required one of two compromises: consolidate everything onto a single vendor's platform (accepting lock-in) or build and maintain custom integrations between platforms (accepting technical debt). Most organisations have ended up doing some of both, and neither is satisfying.
A2A offers a third path: a heterogeneous agent ecosystem where vendors compete on capability rather than proprietary integration. The Salesforce-Google announcement demonstrates this concretely — two major competitors building complementary agents that interoperate rather than forcing customers to choose.
The implications for enterprise architecture are material:
- Best-of-breed without integration penalty. Organisations can select agents based on capability for each use case — a specialist legal AI agent from one vendor, a finance automation agent from another — without paying a large integration cost to connect them.
- Reduced vendor lock-in. When agents communicate through an open standard, replacing one vendor's agent with a better alternative becomes a configuration change rather than a migration project.
- Composable workflows. Complex business processes that span multiple systems — order-to-cash, hire-to-retire, procure-to-pay — can be automated by orchestrating specialist agents rather than requiring a single monolithic AI deployment.
- Faster time to value. Pre-built A2A-compatible agents can be composed into workflows without custom development, dramatically reducing deployment timelines for common use cases.
The 150+ Organisation Ecosystem
The breadth of A2A adoption matters as much as the protocol itself. With Google, Microsoft, AWS, Salesforce, SAP, ServiceNow, Workday, and IBM all committed, A2A covers the core enterprise software stack for most large organisations. A workflow that needs to touch CRM, ERP, ITSM, and HCM systems can do so through A2A without any of those vendors needing bespoke agreements with each other.
The Linux Foundation's stewardship is also significant. Placing A2A under an open-source governance structure rather than a single vendor removes the risk that the protocol is redirected in ways that benefit one participant at the expense of others. The precedent here is Kubernetes — an open standard that now underpins how almost every enterprise runs containerised workloads, regardless of which cloud they use.
A2A is complementary to, not competitive with, Anthropic's Model Context Protocol (MCP), which governs how individual agents access tools and data sources. MCP handles the agent-to-tool layer; A2A handles the agent-to-agent layer. Enterprises building on Claude Managed Agents can use both.
What This Means for Your Platform Decisions
If you are evaluating or re-evaluating your AI platform strategy, A2A changes several of the assumptions that have historically driven those decisions.
Re-evaluate the case for platform consolidation
The traditional argument for standardising on a single AI platform has been integration simplicity — one vendor means one integration layer. A2A weakens this argument considerably. If your primary platforms all support A2A, the integration case for consolidation largely disappears. What remains are genuine product capability differences, pricing, and support quality — all of which are more tractable to evaluate.
Prioritise A2A support in vendor selection
Any AI platform or agent product you evaluate today should be asked directly: do you support A2A, and on what timeline? Vendors that are not on the roadmap for A2A compliance are either planning their own proprietary ecosystem (which re-creates lock-in) or are not serious about enterprise interoperability. Either way, it is a risk factor worth surfacing early.
Think in workflows, not tools
A2A enables a shift in how enterprise AI is designed. Rather than deploying an AI tool that assists with individual tasks, organisations can design end-to-end workflows where multiple specialist agents collaborate. This requires a different conversation internally — from "which AI tool do we buy?" to "which business process do we want to automate end-to-end, and which agents are best suited to each stage?"
Plan your data governance model now
A2A enables agents to collaborate without moving data between systems, but this requires deliberate configuration. Establish which agents are permitted to instruct which other agents, what data each is authorised to access, and how audit trails are maintained across multi-agent workflows. These governance decisions are much easier to make before workflows are in production than after.
The A2A Protocol will not eliminate the complexity of enterprise AI. But it removes one of its most persistent sources of friction — the assumption that effective agentic AI requires a single-vendor environment. For technology leaders evaluating their AI roadmap, that changes the calculus significantly.
Reinvently helps organisations design enterprise AI strategies that are built to last. If you are thinking through your agentic AI architecture, talk to Reinvently.
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