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 ...

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 ...

When your mcp client talks to a server—maybe a retail bot checking inventory levels—they usually do a "handshake" to agree on a secret key. If you use ML-KEM, that handshake stays safe even if a ...

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; ...

The Model Context Protocol (MCP) changes this equation. Think of it as the "USB-C for AI." It's an open standard that allows ...

Claude’s Model Context Protocol promises a new way to handle context across tools and systems. The goal is to improve how models understand and retain information. Testing focuses on real use cases ...

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 ...

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 Model Context Protocol (MCP) usage accelerates, KYND is calling on the insurance industry to rethink its approach to cyber ...

What if the very framework you rely on to power your AI systems becomes the reason your project falters? Model Context Protocols (MCP) are often heralded as the backbone of modern AI, allowing ...