AI agents have become one of the most impactful enterprise innovations in decades. Across industries and functions — from IT and HR to customer service and operations — specialized agents are taking on repetitive tasks, managing workflows and assisting employees with increasing autonomy. The momentum is undeniable: More organizations are deploying agents in production, and the pace of adoption is accelerating.
But this momentum also brings a new challenge: AI agents that can’t talk to each other. While AI agents excel at specific tasks, their design often means they operate in silos.
Many early deployments succeed within a single domain, but stall when scaled across the enterprise. Without interoperability, an AI agent built for one workflow can’t coordinate with an AI agent managing another, or one running on a different model. The result is duplicated work, miscommunication and digital bottlenecks that risk outweighing the benefits.
The future of AI in the enterprise depends not on deploying more isolated agents, but on making it possible for them to work together. That is why interoperability — or the ability for agents to share context, exchange information and coordinate actions across systems — is not simply a technical feature. It’s the foundation for scale.
The Architecture of Interoperability
Without a way to exchange context or coordinate across systems, AI agents create fragmented gains rather than enterprise-wide transformation. Interoperability changes that equation.
At its core, interoperability requires three elements working in concert:
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Open protocols: allow agents to communicate across platforms and vendors.
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Unified data fabrics: provide secure, real-time access to information without costly duplication.
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Centralized orchestration layers: oversee how AI agents interact, ensuring collaboration remains transparent, efficient and accountable.
New protocols like Agent2Agent are at the heart of this architecture. A2A is the open standard designed to let AI agents advertise their capabilities, delegate tasks and coordinate workflows regardless of vendor or underlying technology. With A2A, enterprises can create ecosystems where agents collaborate as seamlessly as human teams, eliminating silos that limit agentic impact.
That is why open standards matter. They don’t just connect systems, they establish a common language that makes scalable, cross-vendor collaboration possible. The power of these types of protocols scaling interoperable agents extends across industries:
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Telecommunications: Predictive agents can anticipate network outages while service agents reroute capacity and customer care agents proactively notify subscribers — all working together to prevent disruptions.
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Manufacturing: Maintenance agents can collaborate with supply chain agents to prevent downtime and manage disruptions in real time.
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Government services: AI service agents could help citizens renew licenses while compliance agents ensure the process follows regulations and privacy requirements.
In every case, the value comes not from isolated efficiency but from orchestrated intelligence.
From Pilots to Operating Models
Eaton, a multinational power management company, illustrates how interoperability transforms AI from pilots into operating systems. With a workforce of 92,000 employees and rising demand for IT and HR services, Eaton knew siloed bots weren’t enough.
By adopting interoperable AI agents powered by A2A, Eaton created a system where agents could coordinate across functions: one to triage requests, another to retrieve policies and knowledge and others to execute routine actions. A shared orchestration layer ensured continuity and eliminated redundant effort.
The results were clear: faster resolution times, fewer tickets and a more conversational, proactive interface for employees. Eaton’s leaders described the shift as moving from one-off automation to a recipe where agents, workflows and generative AI combine to deliver better outcomes.
Critically, they credited success not just to technology, but to strong data quality, governance processes and a clear ROI lens that proved value early and secured support for expansion. Today, Eaton is extending its interoperable AI agents into new domains, scaling a model that turns agentic AI from a promising pilot into an operating system for the enterprise.
Scaling Interconnected Agents Responsibly
For all the technical advances underway, interoperability alone is not enough. Enterprises also need trust. Every decision made by an AI agent must be explainable. Employees need confidence that AI is operating within guardrails; regulators and customers need assurance that systems are accountable.
That’s why governance is the safeguard that makes interoperability sustainable. Transparency into how AI agents arrive at decisions, auditability of their actions, and the ability for leaders to intervene are critical to unlocking interoperability responsibly and at scale.
Here again, A2A plays a role. The protocol is designed with enterprise-grade authentication and auditability in mind, supporting robust governance frameworks. Working with partners to embed these safeguards ensures that agentic collaboration is both powerful and trustworthy.
The Imperative Ahead
The promise of agentic AI is clear: Employees have more time for higher-value work while AI agents handle the routine, the repetitive, and even the predictive. To realize that promise, enterprises must not wait — it is crucial to prioritize interoperability today.
Those who adopt open standards like A2A will be best positioned to move beyond fragmented pilots to AI-powered operating models that scale across the enterprise. They will set the standard for how intelligent systems and human teams collaborate and lead the way in showing how agentic AI can be connected, orchestrated, and governed responsibly.
The question is no longer whether agents should work together. The real question is how quickly organizations will make it possible, and whether they can afford to wait.
