𝗧𝗵𝗲 𝗵𝗶𝗱𝗱𝗲𝗻 𝗰𝗼𝘀𝘁 𝗼𝗳 𝗶𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 𝗰𝗼𝗺𝗽𝗹𝗲𝘅𝗶𝘁𝘆 𝗶𝗻 𝗔𝗜 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 As we scale multi-agent systems and integrate multiple AI models (like ChatGPT, Claude, Gemini, etc.) with enterprise tools—CRM, analytics, internal apps—something critical breaks: 𝗶𝗻𝘁𝗲𝗿𝗼𝗽𝗲𝗿𝗮𝗯𝗶𝗹𝗶𝘁𝘆. This is where the 𝗠𝗼𝗱𝗲𝗹 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗣𝗿𝗼𝘁𝗼𝗰𝗼𝗹 (𝗠𝗖𝗣) comes in. 𝗪𝗶𝘁𝗵𝗼𝘂𝘁 𝗠𝗖𝗣: Each AI agent needs a separate integration with each tool—resulting in an exponential 𝙼 × 𝙽 mess. 𝗪𝗶𝘁𝗵 𝗠𝗖𝗣: A single protocol acts as a unifying layer. Each model and system integrates once with MCP—bringing order, efficiency, and scalability. Now it's simply 𝙼 + 𝙽. This is not just cleaner architecture—it’s 𝗔𝗜 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗮𝘁 𝘀𝗰𝗮𝗹𝗲. I've visualized this transition in the image below to make the value of MCP clear for technical and non-technical teams alike. What do you think—are we heading toward an AI future where 𝗽𝗿𝗼𝘁𝗼𝗰𝗼𝗹-𝗳𝗶𝗿𝘀𝘁 𝗱𝗲𝘀𝗶𝗴𝗻 becomes standard? | 56 comments on LinkedIn