AI agents have become one of the most significant enterprise technologies in years. Across IT, HR, customer service, and operations, specialised agents now handle repetitive tasks, manage workflows, and assist employees with growing autonomy, and the pace of deployment keeps accelerating. That speed, however, exposes a new problem: agents that cannot talk to one another. Because most are designed for a specific task, they tend to operate in silos. A deployment that succeeds in a single domain often stalls when an organisation tries to scale it across the enterprise, because an agent built for one workflow cannot coordinate with an agent managing another, or with one running on a different model. The result is duplicated work, miscommunication, and the kind of friction that erodes the return an agent was meant to deliver.
The architecture of interoperability
Making agents work together takes more than connecting them point to point. Two structural pieces tend to recur. Shared data layers give agents a common, governed source of truth so they are not each maintaining their own copy of the same information, which avoids costly duplication. Orchestration layers sit above the agents to monitor how they interact, keeping collaboration transparent, efficient, and accountable. Tying these together are open protocols that let agents from different vendors understand one another.
The most prominent of these is the Agent2Agent (A2A) protocol. Created by Google in 2025 and now hosted as a vendor-neutral project under the Linux Foundation with backing from more than a hundred companies, A2A is an open standard that lets agents advertise their capabilities, delegate tasks, and coordinate workflows regardless of vendor or underlying technology. A complementary standard, the Model Context Protocol (MCP) introduced by Anthropic, addresses the related problem of connecting a single agent to external tools and data sources. Together, such standards do more than wire systems together; they establish a common language that makes scalable, cross-vendor collaboration possible.
From pilot to operating model
Interoperability is what turns a promising pilot into an enterprise-wide capability. In telecommunications, for example, predictive agents might anticipate a network outage while other agents reallocate capacity and customer-service agents proactively notify affected users, each handing off to the next. Organisations that have scaled this way often credit their success less to the technology itself than to disciplined foundations: strong data quality, clear governance processes, and an explicit return-on-investment lens that proves early value and builds support for expansion. The pattern is consistent: agents become an operating model for the business only once those foundations are in place.
Scaling interconnected agents responsibly
For all the technical progress, interoperability alone is not enough; enterprises also need trust. Decisions made by an agent should be explainable, employees need confidence that agents operate within guardrails, and regulators and customers need assurance that the systems are accountable. Governance is the safeguard that makes interoperability sustainable: transparency into how agents reach decisions, auditability of their actions, and the ability for leaders to intervene responsibly and at scale. Mature protocols help here too. A2A, for instance, was designed with enterprise-grade authentication and auditability in mind, which supports rather than replaces an organisation’s own governance framework.
Limitations and what to watch
The case for interoperability is strong, but it should be read with some caution. The standards themselves are young: A2A and MCP have gained rapid adoption, yet the surrounding tooling, security practices, and real-world track record are still maturing, and competing or overlapping protocols could fragment the landscape before it consolidates. Connecting many agents also enlarges the attack surface and the potential blast radius of errors, so an interoperable system without strong authentication, monitoring, and human oversight can fail in more places, not fewer. Vendor framing matters as well: much of the enthusiasm comes from companies that benefit from a particular standard’s adoption, so claims of seamless collaboration deserve testing against an organisation’s own workflows. None of this argues against interoperability; it argues for adopting it deliberately, with governance and security treated as first-class concerns rather than afterthoughts.
The imperative
The promise of agentic AI is straightforward: employees spend more time on high-value work while agents handle routine, repetition, and forecasting. Realising that promise at scale depends on agents being able to cooperate, which is why interoperability is becoming a priority rather than a nice-to-have. Organisations that adopt open standards thoughtfully will be better positioned to move beyond fragmented pilots toward a connected operating model, provided they pair that connectivity with the orchestration and governance that keep it controllable. The question is no longer whether agents should work together, but how to connect them in a way that is both powerful and accountable, much as with any agent project that aims to reach production or any multi-agent system built to scale.
