When I was delivering a session on Multi Agent AI Ecosystem at Huddle, an event organized by Kerala Startup Mission last year, a question came up —”How can we build AI agents that not only connect but also work together ?”. A few days later, in another session with a NASSCOM group of fellow AI enthusiasts, the same debate resurfaced.

In both the forums, we all acknowledged the difficulty and agreed that the protocols we had – like Knowledge Query and Manipulation Language (KQML) and Foundation for Intelligent Physical Agents (FIPA)—helped, but they had their limitations.

👉 This is why Model Context Protocol (MCP) is getting so much attention now.

Building an AI agent ecosystem today is like running a company where different teams—marketing, engineering, and finance—each work in silos. They all have valuable data, but without a shared project management system, things get duplicated, key insights get lost, and efficiency drops.

Now imagine this analogy with AI models. Each large language model (LLM) has its own way of processing and storing context. They don’t naturally share information or build on each other’s knowledge. This makes multi-agent collaboration difficult.

This reminds me of how the internet worked before Transmission Control Protocol/Internet Protocol (TCP/IP). Back then, different networks couldn’t talk to each other efficiently. TCP/IP changed that by creating a standard protocol, making seamless communication possible.

MCP is doing something similar for AI agents.

What does MCP solve?
🔹 Context persistence – AI agents won’t forget past interactions, making them more useful over time.
🔹 Efficient Multi-Agent workflows – Agents can divide work intelligently instead of repeating efforts.
🔹 Standardized communication – Different AI models can work together without compatibility issues.

👉 How is MCP different from other protocols?

We did have AI communication protocols before—KQML, FIPA, RESTful APIs, and Simple Public Key Infrastructure (SPKI/SDSI)— that were designed for specific communication needs.

But these don’t handle shared memory or deep agent collaboration like MCP does. MCP is built for LLM-based AI agents, ensuring they can store, retrieve, and build on context dynamically—just like how humans remember and build upon past experiences in a conversation.

Just like TCP/IP enabled the internet, I strongly believe that MCP can unlock a new era of autonomous AI ecosystems. Instead of isolated models generating responses independently, we’ll have AI agents that work together, share knowledge, and continuously learn from one another.

The needle has moved beyond “smart AI” to –> “AI that truly collaborates”.