July 2, 2025 • 6 min read
AI is evolving at breakneck speed. We now have AI “agents” that can draft emails, debug code, manage calendars, and more – essentially digital coworkers. But until recently, most of these AI agents have been working in isolation. They struggle to access all the data they need, and they certainly don’t speak a common language to collaborate with each other.
In other words, each AI has been an island of intelligence, cut off from other systems. This is becoming a real hurdle as businesses try to integrate AI everywhere. How do we fix that? By teaching our AI agents to talk – both to our data and to each other. Enter two emerging open standards: MCP (Model Context Protocol) and A2A (Agent-to-Agent Protocol).
The current state of AI is a bit like the early days of the internet – lots of potential, but also lots of fragmentation. Companies are deploying AI assistants for countless tasks, yet these systems often can’t share information or work together out-of-the-box.
Important business data sits in siloed databases and apps that an AI can’t reach, and if you have multiple AI tools, they can’t coordinate actions like a human team could. This lack of interoperability limits how useful and autonomous AI agents can really be.
Industry leaders have recognized this problem.
Anthropic noted that even “the most sophisticated models are constrained by their isolation from data—trapped behind information silos and legacy systems.”
Google observed that agents need to “interoperate with each other, even if they were built by different vendors or in a different framework,” which will greatly boost their autonomy and productivity.
Think of MCP as a power outlet that lets AI systems plug into all sorts of external tools and databases. It’s an open standard designed to provide a “universal…single protocol” for linking AI agents with the data and services they need.
Instead of building one-off integrations for every app, developers can use MCP as a consistent interface. An AI agent using MCP can securely query a knowledge base, fetch a file, call an API, or invoke a cloud service on the fly—all through a standardized mechanism.
Engineers say, “it’s like giving your AI access to a toolbox, but without hardwiring the tools into the model.” It enables live, structured data access, making AI responses more relevant and up-to-date.
Early adopters have built connectors for Slack, GitHub, Google Drive, databases, and more. For instance, a support agent could use MCP to pull the latest order status from internal systems in real time.
While MCP solves the data access challenge, A2A (Agent-to-Agent Protocol) tackles communication. It enables different AI agents to discover one another, share data, and coordinate tasks—even across vendors and cloud platforms.
For example, your HR AI could request help from an analytics AI. Thanks to A2A, they speak a common language, exchanging requests and services through secure JSON messages.
Each agent can advertise its capabilities via an “Agent Card,” a digital profile that others can read and act on. A2A ensures secure communication using web standards like HTTP and OAuth, making multi-agent workflows realistic and safe for enterprise use.
Google launched A2A in 2025 with backing from over 50 partners, signaling strong momentum for agent collaboration standards.
MCP and A2A aren’t competitors—they’re complements. One connects AI to tools and data, the other connects AI to other agents. Combined, they create the foundation for truly collaborative, context-aware AI systems.
Imagine a smart auto-repair shop: MCP lets an AI control IoT-enabled tools, while A2A allows it to consult a diagnostic expert agent or parts supplier in real time. That’s the future these protocols are building.
We’re still in the early days, and widespread adoption is key. But if the industry aligns around these standards, AI agents could become as plug-and-play as today’s apps on a Wi-Fi network.
For IT leaders, engineers, and AI builders, keep an eye on MCP and A2A. These might just be the missing glue to make AI agents smarter, safer, and more useful at scale.
AI agents are growing up. It’s about time they start talking.
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