There’s a quiet revolution happening in AI—and if you’re a marketer, you might’ve missed the headline. It’s called the Multi-Agent Communication Protocol (MCP for short). It sounds technical, and it is. But don’t let the acronym fool you. This could be one of the most important shifts in AI since ChatGPT broke the internet.Why? Because it’s not just about what AI can do. It’s about what AI can now do together.
So, what is MCP?
An MCP—Multi-Agent Communication Protocol—is a common language that allows different AI agents to talk to one another.
A good way to think about it, is that a similar important shift happened in the early days of the internet. In its earliest stages, the internet needed a way to facilitate the sharing of information among scientists using the internet, and this wasn’t exactly an easy thing. Everyone was trying to do different things, and there was no consistency. Along came hyper text transfer protocol (HTTP – recognise it now?) as a protocol for retrieving linked resources (like web pages) across the internet.
MCP is a framework that lets AI agents (like the ones you’d build with OpenAI’s GPTs, Meta’s LLaMa models, or open-source tools) collaborate, share tasks, and solve complex problems by working as a team. This could be just as fundamental to the new reality we face as HTTP was for the way we use the internet today.
Until now, most AI tools have been isolated. You prompt one chatbot. You get a single answer. But real-world business challenges rarely sit inside neat silos. Marketing overlaps with sales, product, service, finance. One bot can’t do it all.
MCPs change the game by making it possible for multiple agents—with different specialisms and skills—to operate as a coordinated system. It’s the beginning of an AI stack that actually behaves like a well-run team.
Where did it come from?
MCPs didn’t just appear out of nowhere. They were introduced in early 2024 by OpenAI as part of their developer previews, and they’ve been gathering momentum fast. The idea? To create a standardised protocol that different agents could adopt to work together—across tools, models, and platforms.
It’s model-agnostic. It doesn’t matter if one agent is GPT-4o and the other is running on Claude or Mistral. If they speak MCP, they can collaborate.
This is a subtle but important shift. It moves AI away from being a single, siloed brain, and towards being a network of specialised brains that can pass tasks, share context, and coordinate output.
And if that sounds like a well-oiled marketing team… well, exactly.
What does this mean for marketers?
It means AI is now collaborative. It means marketers can build systems of AI agents that work together across the customer journey. And it means your marketing stack is about to become a whole lot smarter.
Let’s look at a few use cases:
- Campaign orchestration: Imagine one agent that handles audience segmentation, another that drafts content, and another that schedules and posts across platforms. MCP lets these agents talk to each other. When the audience agent flags a trend, the content agent adjusts copy and the publishing agent shifts the schedule—automatically.
- Brand governance at scale: One agent watches tone of voice. Another checks performance. A third one analyses competitor messaging. They collaborate to ensure consistency, adapt based on market shifts, and feed each other insights.
- Real-time customer interaction: A service bot chats with a customer. A recommendation bot feeds it relevant products. A pricing agent adjusts offers based on stock or demand. The customer sees one coherent conversation. Under the hood, three AI agents are working in harmony.
This is orchestration. This is automation. This is AI that acts more like a team than a tool.
Where to Start
Like all things AI, the key is to experiment early, and learn fast. You don’t need a full multi-agent setup out of the gate. But you do need to start thinking systemically. Here’s where we’d recommend focusing:
- Start small, think modular
Don’t build the ultimate AI team overnight. Build one agent with a clear job (e.g. content generation for email). Then consider what other agents it might benefit from talking to. - Get familiar with MCP
If you’re building custom agents or working with developers, make sure they understand the protocol. OpenAI has documentation. So do others. This will be the lingua franca of agent ecosystems. - Map your marketing processes
Where are the handoffs? The inefficiencies? The repetitive tasks? These are places where multiple agents working together can add value.
Design for interaction
Not every agent needs a user interface. Some work best in the background, collaborating with other agents. That’s okay. Think roles, not screens.
Three Handy Tips for Marketers:
Start treating AI like a team member. Give it a role. Give it metrics. Let it collaborate. MCP will take care of the rest.
Ask your tech teams about MCP. If they haven’t heard of it, point them to the documentation.
Try building a two-agent test. Even a basic example (like brief + write) will teach you the power of agent coordination.
Final Thought
MCPs are like the TCP/IP of agentic AI. The plumbing. The protocol. The part that most people won’t notice—but which will change everything.
Marketing leaders who get ahead of this curve won’t just use AI. They’ll deploy AI in systems that think, adapt and act like high-performing teams. And in a world of constant change, that might just be the edge that matters most.
Cognitive Union is a progressive, boutique learning and performance consultancy. We work with forward-thinking businesses. Transforming their people. Shaping their culture. Helping them embrace change and take on the world. Find this blog useful? Sign up to our email newsletter (bottom of this page) where you can receive articles like this and other insights (not publically published), and you can also follow us on LinkedIn.