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 ...

Artificial intelligence is still rapidly evolving, though there remains one fundamental constraint on its effectiveness: the provision of authentic, immediate, and permissioned access to the relevant ...

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 ...

The framework provided by MCP allows agents to access and engage with databases, tools, apps and agents in real time in a united way.

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 ...

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, ...

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 ...

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 ...

As AI agents begin operating across enterprise systems, MCP is emerging as the connective layer IT leaders can’t afford to ignore.