Agent Interoperability: Why It’s the Make-or-Break Decision for the Future of AI

Let’s face it—building a smart AI agent isn’t the hard part anymore. The real challenge is making sure that your agents can actually work together, plug into your enterprise systems, and evolve over time without breaking everything downstream. In a world where AI is becoming more agentic, more distributed, and more embedded into how business gets done, interoperability isn’t a technical feature—it’s a survival skill.

But here’s where things get tricky: the standards are still maturing, the tools are evolving by the month, and everyone has a slightly different view of what “interoperability” actually means. Should agents communicate via APIs? Use a shared context like Anthropic’s MCP? Coordinate like a team, as Google envisions with its Agent-to-Agent model? Or is there something else entirely?

Let’s break down where we are, what’s changing, and why your architectural decisions today will determine whether your AI investments scale—or stall.

APIs: Reliable, Predictable… and a Bit Too Rigid

Let’s start with the classic: APIs. They’ve served us well for years. They’re secure, predictable, and easy to govern. In healthcare, especially, they’re everywhere—from calling up a patient’s chart in the EHR to verifying a prescription or submitting a claim.

But when it comes to agentic systems, APIs show their age quickly.

Each API you integrate becomes another piece of code you have to maintain. Every time a tool changes, a form gets updated, or a workflow shifts, your agents need a new instruction set. It’s like teaching your AI to follow a recipe—but any time the ingredients change, it forgets how to cook.

So yes, APIs still matter—especially in high-trust environments where traceability and control are non-negotiable. But if you’re trying to build adaptive agents that can evolve with your business, APIs alone just won’t cut it.

MCP: Giving Agents the Full Picture, Not Just Instructions

Now let’s talk about something newer: Anthropic’s Model Context Protocol, or MCP. It’s a completely different way of thinking.

Instead of hardcoding every action, you give the agent a structured, well-organized view of its environment. That includes tool descriptions, business rules, preferences, memory—basically, everything it needs to make decisions. The model figures out what to do based on what it knows, in real time.

Think of it like this: with APIs, you’re giving the agent a set of buttons to press. With MCP, you’re handing it a map, a compass, and a destination, then letting it choose the best route.

This makes MCP incredibly powerful for clinical decision support, real-time medical summarization, or research assistance, where rigid workflows aren’t realistic and context really matters. It’s lighter on code, faster to iterate, and far more adaptable.

Of course, there’s a tradeoff. You have to trust the model’s ability to reason well. And because you’re not explicitly telling it how to behave, things like testing, auditing, and governance need to evolve. You’re shifting from controlling the agent’s every move to influencing its behavior through information design.

Still, if you’re building for flexibility and speed, MCP is one of the most future-resilient bets you can make right now.

A2A: AI as a Team Sport

Then there’s Agent-to-Agent (A2A)—Google’s vision for a world where agents collaborate like teammates.

In this setup, each agent has its own specialty, memory, and role. One might handle information gathering, another manages compliance, a third executes actions—and they talk to each other, hand off tasks, and coordinate to achieve a shared outcome.

Sound familiar? It’s how humans get things done.

This model is perfect for complex, multi-role scenarios like clinical trial coordination, drug development workflows, or even enterprise-wide knowledge management, where no single agent can—or should—own the entire task.

The upside is obvious: you get modularity, scalability, and the ability to evolve or replace agents without re-architecting everything.

But it’s not plug-and-play. Messaging between agents requires structure. You’ll need ways to manage agent identity, trust boundaries, and conversation history. It’s more engineering up front—but once it’s in place, it gives you serious leverage.

What Else Should Be on Your Radar?

MCP and A2A aren’t the only games in town. If you’re architecting for the long haul, there are a few other models you should absolutely be aware of.

First, there’s the toolchain model—think LangChain or Semantic Kernel. These frameworks let you choreograph how an agent behaves: first retrieve the data, then validate it, then generate a response. It’s excellent when you need control and repeatability, like automating documentation or processing regulatory reports. But as soon as workflows become more dynamic, these systems start to feel rigid—like trying to build with LEGO bricks when you need clay.

Then you’ve got memory-centric architectures. These systems let agents “remember” things: past interactions, patient preferences, research trails. That memory can live in vector databases or long-term storage, and the agent retrieves it as needed. It’s hugely valuable for personalization, longitudinal reasoning, or any use case where the agent shouldn’t start from zero every time. But it requires good design—because too much memory, or the wrong kind, creates noise instead of insight.

And don’t overlook knowledge-based systems like ontologies and knowledge graphs. In highly regulated domains like healthcare, grounding your agents in real-world entities and relationships—drug-drug interactions, disease pathways, biomarker hierarchies—ensures they reason accurately, not just statistically. It’s a heavy lift to set up, but for research, regulatory intelligence, or high-stakes diagnostics, it’s essential.

So What: What Should You Build On?

Honestly? Probably all of the above.

APIs are your control layer—they anchor your agents in the real world. MCP is your flexibility layer—it lets agents think and act without brittle integrations. A2A is your coordination layer—it’s how your agents scale as a team.

Toolchains help with deterministic logic. Memory architectures add long-term context. And knowledge graphs provide structure and semantics.

But if you’re thinking about future-proofing, here’s the simple truth: the more your system relies on hardcoded paths and one-off integrations, the more likely it is to break—or become irrelevant—as your needs evolve.

Now What: Interoperability Isn’t Optional

The real cost of getting interoperability wrong isn’t just complexity—it’s lost momentum. It’s every engineer hour spent fixing brittle code instead of building value. It’s every promising AI initiative that never scales because it’s too hard to integrate, too slow to adapt, or too siloed to collaborate.

MCP and A2A give you the flexibility, composability, and adaptability to keep pace with the AI ecosystem—not just today, but as it evolves. They’re not without challenges, but they align better with where the future is clearly headed: intelligent systems that reason together, act autonomously, and integrate fluidly across the enterprise.

So if you’re building for tomorrow, don’t just ask: “Can my agents do the job?”

Ask instead: “Can they do the job together, at scale, and without breaking every time the world changes?”

Because in the age of AI, how well your agents work together may matter more than how smart they are individually.... just like our teams.