Agentic AI is radically transforming software, moving beyond simple text generation models. They can reason, plan and execute complex tasks by interacting with external systems and APIs. But for most organizations, this powerful potential remains just out of reach, because the most advanced AI models are often isolated from the very tools, data and internal systems where real business value is created, trapped behind a wall of incompatible APIs and proprietary connections.
The Model Context Protocol, or MCP, is the standard designed to solve this problem. Think of it as a universal translator or a “USB-C for AI.” Itʼs an open standard that creates a common language, allowing AI agents to securely and seamlessly plug into the diverse landscape of enterprise applications. But adopting a new protocol — let alone implementation — is never as simple as flipping a switch. It essentially requires a clear understanding of both the architectural changes and the practical hurdles on the ground.
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