As organizations push AI systems into production, IT teams are asking how to make models more dependable, secure and useful in real-world workflows. One approach gaining traction is the Model Context ...

Imagine you’ve trained or fine‑tuned a chatbot or an LLM, and it can chat comfortably without any serious hiccups. You feed it a prompt and it responds. However, it’s stuck in a bubble: It only knows ...

A new GitHub repo serves as a lab for creating a Model Context Protocol (MCP) Server and using it in Microsoft Copilot Studio. The MCP, originated by AI leader Anthropic and taken open source, is a ...

As the development of AI tools accelerates, organizations are under increasing pressure to move models from prototype to production securely and with scalability. Behind the scenes, managing AI models ...

Making inherently probabilistic and isolated large language models (LLMs) work in a context-aware, deterministic way to take real-world decisions and actions has proven to be a hard problem. As we ...

Imagine a world where your AI tools don’t just work for you but work with each other—seamlessly, intelligently, and without the frustration of endless custom integrations. This isn’t a distant dream; ...

What if the next generation of AI systems could not only understand context but also act on it in real time? Imagine a world where large language models (LLMs) seamlessly interact with external tools, ...

The Model Context Protocol (MCP) changes this equation. Think of it as the "USB-C for AI." It's an open standard that allows us to plug our AI models directly into our security stack (SIEM, EDR, ...

AI innovation today is moving faster than ever before, with leaps and bounds being made in the field on what seems like a weekly basis. Further to innovation that is directly related to or produced by ...

An interface between an AI language model and external sources such as a database. The Model Context Protocol server (MCP server) determines what the model can access. The MCP client, typically an AI ...