Interoperability is critical because AI agents have to be able to share tools, context, and tasks securely across boundaries.
AI agents are no longer a future concept. They’re already in the enterprise, writing documents, triaging support tickets, and onboarding new hires. But most of them share a critical flaw: they don’t work together.
In the real world, enterprise workflows cross systems, vendors, and domains. HR lives in Workday, finance in Oracle, tickets in Jira, or the supply chain in SAP. An AI agent that operates only inside one system is like a brilliant employee locked in a single conference room.
That’s why interoperability is the next big test for AI agents. It’s not enough for them to be smart. They have to be able to share tools, context, and tasks securely across boundaries.
The limits of siloed automation
Today, most agents run in isolation. Even if you deploy multiple agents, they often can’t talk to each other because they lack a shared protocol. That means:
- Agents duplicate work instead of coordinating.
- Developers rebuild the same integrations over and over.
- Automation remains brittle and hard to scale.
This is the last-mile problem of enterprise AI. The models are capable, but the wiring isn’t there. Without a way to connect, they can’t produce cohesive, end-to-end workflows.
See also: MCP: Enabling the Next Phase of Enterprise AI
Why open protocols matter for AI agents
Two protocols, Model Context Protocol (MCP) and Agent-to-Agent (A2A), point the way forward.
MCP solves model-to-tool communication. It gives agents a universal way to call tools, no matter the vendor. A2A solves agent-to-agent communication. Originally introduced by Google, A2A lets agents discover each other, understand each other’s capabilities, and delegate tasks, even if they run on different platforms.
Here’s what that looks like in practice. Imagine an “RFP Writer” agent spins up for a procurement team:
1) Discovery: It publishes an A2A Agent Card and scans the enterprise registry. It finds a supplier-pricing agent on another platform.
2) Capability Matching: It sees that the supplier agent can provide real-time pricing.
3) Delegation: It sends a structured request for line-item pricing.
4) Tool Invocation: The supplier agent calls a NetSuite microservice via MCP, retrieves the data, and returns it.
5) Governance: Every step is logged and policy-checked through a governance layer.
Instead of brittle point-to-point integrations, you get a modular, policy-compliant workflow that scales.
The path forward
In the early SaaS era, companies piled on dozens of point solutions that didn’t talk to each other. Years (and millions) later, they stitched them together with middleware and integration platforms. With AI agents, we have a chance to skip that mess if we treat interoperability as a first-class problem now.
For enterprise AI agents to deliver, we need shared data models so “customer” means the same thing everywhere; open, secure protocols so agents can connect without custom plumbing; role- and context-aware permissions so every action is compliant; and standardized human-in-the-loop workflows so agents can hand off seamlessly.
The future of enterprise AI hinges on an ecosystem where agents can act, delegate, and collaborate across teams, tools, and trust boundaries. Interoperability is how we get there. And the time to solve it is now.
