Traditional AI inference is based on static large language models (LLMs) that can’t see past their training cutoff dates. If something isn’t in the training dataset, then as far as the model’s concerned, it simply doesn’t exist. In contrast, AI agents can pull real-time data from multiple external sources. These agents actively seek out new information in response to environmental changes, without the need for human intervention. Agentic AI opens a wide range of new applications across industries, from real-time patient monitoring in healthcare to high-frequency trading in financial markets. In these use cases, AI agents can connect to data sources…

The post What Is the Model Context Protocol (MCP)? How Will it Enable the Future of Agentic AI? appeared first on Interconnections - The Equinix Blog.